| Title: | High Performance Implementation of the Naive Bayes Algorithm |
|---|---|
| Description: | In this implementation of the Naive Bayes classifier following class conditional distributions are available: 'Bernoulli', 'Categorical', 'Gaussian', 'Poisson', 'Multinomial' and non-parametric representation of the class conditional density estimated via Kernel Density Estimation. Implemented classifiers handle missing data and can take advantage of sparse data. |
| Authors: | Michal Majka [aut, cre] |
| Maintainer: | Michal Majka <[email protected]> |
| License: | GPL-2 |
| Version: | 1.0.0 |
| Built: | 2025-03-09 05:56:45 UTC |
| Source: | https://github.com/majkamichal/naivebayes |
bernoulli_naive_bayes is used to fit the Bernoulli Naive Bayes model in which all class conditional distributions are assumed to be Bernoulli and be independent.
bernoulli_naive_bayes(x, y, prior = NULL, laplace = 0, ...)bernoulli_naive_bayes(x, y, prior = NULL, laplace = 0, ...)
x |
matrix with numeric 0-1 predictors (matrix or dgCMatrix from Matrix package). |
y |
class vector (character/factor/logical). |
prior |
vector with prior probabilities of the classes. If unspecified, the class proportions for the training set are used. If present, the probabilities should be specified in the order of the factor levels. |
laplace |
value used for Laplace smoothing (additive smoothing). Defaults to 0 (no Laplace smoothing). |
... |
not used. |
This is a specialized version of the Naive Bayes classifier, in which all features take on numeric 0-1 values and class conditional probabilities are modelled with the Bernoulli distribution.
The Bernoulli Naive Bayes is available in both, naive_bayes and bernoulli_naive_bayes. The latter provides more efficient performance though. Faster calculation times come from restricting the data to a numeric 0-1 matrix and taking advantage of linear algebra operations. Sparse matrices of class "dgCMatrix" (Matrix package) are supported in order to furthermore speed up calculation times.
The bernoulli_naive_bayes and naive_bayes() are equivalent when the latter uses "0"-"1" character matrix.
The missing values (NAs) are omited while constructing probability tables. Also, the corresponding predict function excludes all NAs from the calculation of posterior probabilities (an informative warning is always given).
bernoulli_naive_bayes returns an object of class "bernoulli_naive_bayes" which is a list with following components:
data |
list with two components: |
levels |
character vector with values of the class variable. |
laplace |
amount of Laplace smoothing (additive smoothing). |
prob1 |
matrix with class conditional probabilities for the value 1. Based on this matrix full probability tables can be constructed. Please, see |
prior |
numeric vector with prior probabilities. |
call |
the call that produced this object. |
Michal Majka, [email protected]
naive_bayes, predict.bernoulli_naive_bayes, plot.bernoulli_naive_bayes, tables, get_cond_dist, %class%
# library(naivebayes) ### Simulate the data: set.seed(1) cols <- 10 ; rows <- 100 ; probs <- c("0" = 0.9, "1" = 0.1) M <- matrix(sample(0:1, rows * cols, TRUE, probs), nrow = rows, ncol = cols) y <- factor(sample(paste0("class", LETTERS[1:2]), rows, TRUE, prob = c(0.3,0.7))) colnames(M) <- paste0("V", seq_len(ncol(M))) laplace <- 0 ### Train the Bernoulli Naive Bayes bnb <- bernoulli_naive_bayes(x = M, y = y, laplace = laplace) summary(bnb) # Classification head(predict(bnb, newdata = M, type = "class")) # head(bnb %class% M) # Posterior probabilities head(predict(bnb, newdata = M, type = "prob")) # head(bnb %prob% M) # Parameter estimates coef(bnb) ### Sparse data: train the Bernoulli Naive Bayes library(Matrix) M_sparse <- Matrix(M, sparse = TRUE) class(M_sparse) # dgCMatrix # Fit the model with sparse data bnb_sparse <- bernoulli_naive_bayes(M_sparse, y, laplace = laplace) # Classification head(predict(bnb_sparse, newdata = M_sparse, type = "class")) # Posterior probabilities head(predict(bnb_sparse, newdata = M_sparse, type = "prob")) # Parameter estimates coef(bnb_sparse) ### Equivalent calculation with general naive_bayes function. ### (no sparse data support by naive_bayes) # Make sure that the columns are factors with the 0-1 levels df <- as.data.frame(lapply(as.data.frame(M), factor, levels = c(0,1))) # sapply(df, class) nb <- naive_bayes(df, y, laplace = laplace) summary(nb) head(predict(nb, type = "prob")) # Obtain probability tables tables(nb, which = "V1") tables(bnb, which = "V1") # Visualise class conditional Bernoulli distributions plot(nb, "V1", prob = "conditional") plot(bnb, which = "V1", prob = "conditional") # Check the equivalence of the class conditional distributions all(get_cond_dist(nb) == get_cond_dist(bnb))# library(naivebayes) ### Simulate the data: set.seed(1) cols <- 10 ; rows <- 100 ; probs <- c("0" = 0.9, "1" = 0.1) M <- matrix(sample(0:1, rows * cols, TRUE, probs), nrow = rows, ncol = cols) y <- factor(sample(paste0("class", LETTERS[1:2]), rows, TRUE, prob = c(0.3,0.7))) colnames(M) <- paste0("V", seq_len(ncol(M))) laplace <- 0 ### Train the Bernoulli Naive Bayes bnb <- bernoulli_naive_bayes(x = M, y = y, laplace = laplace) summary(bnb) # Classification head(predict(bnb, newdata = M, type = "class")) # head(bnb %class% M) # Posterior probabilities head(predict(bnb, newdata = M, type = "prob")) # head(bnb %prob% M) # Parameter estimates coef(bnb) ### Sparse data: train the Bernoulli Naive Bayes library(Matrix) M_sparse <- Matrix(M, sparse = TRUE) class(M_sparse) # dgCMatrix # Fit the model with sparse data bnb_sparse <- bernoulli_naive_bayes(M_sparse, y, laplace = laplace) # Classification head(predict(bnb_sparse, newdata = M_sparse, type = "class")) # Posterior probabilities head(predict(bnb_sparse, newdata = M_sparse, type = "prob")) # Parameter estimates coef(bnb_sparse) ### Equivalent calculation with general naive_bayes function. ### (no sparse data support by naive_bayes) # Make sure that the columns are factors with the 0-1 levels df <- as.data.frame(lapply(as.data.frame(M), factor, levels = c(0,1))) # sapply(df, class) nb <- naive_bayes(df, y, laplace = laplace) summary(nb) head(predict(nb, type = "prob")) # Obtain probability tables tables(nb, which = "V1") tables(bnb, which = "V1") # Visualise class conditional Bernoulli distributions plot(nb, "V1", prob = "conditional") plot(bnb, which = "V1", prob = "conditional") # Check the equivalence of the class conditional distributions all(get_cond_dist(nb) == get_cond_dist(bnb))
coef is a generic function which extracts model coefficients from specialized Naive Bayes objects.
## S3 method for class 'bernoulli_naive_bayes' coef(object, ...) ## S3 method for class 'multinomial_naive_bayes' coef(object, ...) ## S3 method for class 'poisson_naive_bayes' coef(object, ...) ## S3 method for class 'gaussian_naive_bayes' coef(object, ...)## S3 method for class 'bernoulli_naive_bayes' coef(object, ...) ## S3 method for class 'multinomial_naive_bayes' coef(object, ...) ## S3 method for class 'poisson_naive_bayes' coef(object, ...) ## S3 method for class 'gaussian_naive_bayes' coef(object, ...)
object |
object of class inheriting from |
... |
not used. |
Coefficients extracted from the specialised Naive Bayes objects in form of a data frame.
Michal Majka, [email protected]
bernoulli_naive_bayes, multinomial_naive_bayes, poisson_naive_bayes, gaussian_naive_bayes
data(iris) y <- iris[[5]] M <- as.matrix(iris[-5]) ### Train the Gaussian Naive Bayes gnb <- gaussian_naive_bayes(x = M, y = y) ### Extract coefficients coef(gnb) coef(gnb)[c(TRUE,FALSE)] # only means coef(gnb)[c(FALSE,TRUE)] # only standard deviationsdata(iris) y <- iris[[5]] M <- as.matrix(iris[-5]) ### Train the Gaussian Naive Bayes gnb <- gaussian_naive_bayes(x = M, y = y) ### Extract coefficients coef(gnb) coef(gnb)[c(TRUE,FALSE)] # only means coef(gnb)[c(FALSE,TRUE)] # only standard deviations
gaussian_naive_bayes is used to fit the Gaussian Naive Bayes model in which all class conditional distributions are assumed to be Gaussian and be independent.
gaussian_naive_bayes(x, y, prior = NULL, ...)gaussian_naive_bayes(x, y, prior = NULL, ...)
x |
numeric matrix with metric predictors (matrix or dgCMatrix from Matrix package). |
y |
class vector (character/factor/logical). |
prior |
vector with prior probabilities of the classes. If unspecified, the class proportions for the training set are used. If present, the probabilities should be specified in the order of the factor levels. |
... |
not used. |
This is a specialized version of the Naive Bayes classifier, in which all features take on real values (numeric/integer) and class conditional probabilities are modelled with the Gaussian distribution.
The Gaussian Naive Bayes is available in both, naive_bayes and gaussian_naive_bayes.The latter provides more efficient performance though. Faster calculation times come from restricting the data to a matrix with numeric columns and taking advantage of linear algebra operations. Sparse matrices of class "dgCMatrix" (Matrix package) are supported in order to furthermore speed up calculation times.
The gaussian_naive_bayes and naive_bayes() are equivalent when the latter is used with usepoisson = FALSE and usekernel = FALSE; and a matrix/data.frame contains numeric columns.
The missing values (NAs) are omited during the estimation process. Also, the corresponding predict function excludes all NAs from the calculation of posterior probabilities (an informative warning is always given).
gaussian_naive_bayes returns an object of class "gaussian_naive_bayes" which is a list with following components:
data |
list with two components: |
levels |
character vector with values of the class variable. |
params |
list with two matrices, first containing the class conditional means and the second containing the class conditional standard deviations. |
prior |
numeric vector with prior probabilities. |
call |
the call that produced this object. |
Michal Majka, [email protected]
naive_bayes, predict.gaussian_naive_bayes, plot.gaussian_naive_bayes, tables, get_cond_dist, %class%
# library(naivebayes) set.seed(1) cols <- 10 ; rows <- 100 M <- matrix(rnorm(rows * cols, 100, 15), nrow = rows, ncol = cols) y <- factor(sample(paste0("class", LETTERS[1:2]), rows, TRUE, prob = c(0.3,0.7))) colnames(M) <- paste0("V", seq_len(ncol(M))) ### Train the Gaussian Naive Bayes gnb <- gaussian_naive_bayes(x = M, y = y) summary(gnb) # Classification head(predict(gnb, newdata = M, type = "class")) # head(gnb %class% M) # Posterior probabilities head(predict(gnb, newdata = M, type = "prob")) # head(gnb %prob% M) # Parameter estimates coef(gnb) ### Sparse data: train the Gaussian Naive Bayes library(Matrix) M_sparse <- Matrix(M, sparse = TRUE) class(M_sparse) # dgCMatrix # Fit the model with sparse data gnb_sparse <- gaussian_naive_bayes(M_sparse, y) # Classification head(predict(gnb_sparse, newdata = M_sparse, type = "class")) # Posterior probabilities head(predict(gnb_sparse, newdata = M_sparse, type = "prob")) # Parameter estimates coef(gnb_sparse) ### Equivalent calculation with general naive_bayes function. ### (no sparse data support by naive_bayes) nb <- naive_bayes(M, y) summary(nb) head(predict(nb, type = "prob")) # Obtain probability tables tables(nb, which = "V1") tables(gnb, which = "V1") # Visualise class conditional Gaussian distributions plot(nb, "V1", prob = "conditional") plot(gnb, which = "V1", prob = "conditional") # Check the equivalence of the class conditional distributions all(get_cond_dist(nb) == get_cond_dist(gnb))# library(naivebayes) set.seed(1) cols <- 10 ; rows <- 100 M <- matrix(rnorm(rows * cols, 100, 15), nrow = rows, ncol = cols) y <- factor(sample(paste0("class", LETTERS[1:2]), rows, TRUE, prob = c(0.3,0.7))) colnames(M) <- paste0("V", seq_len(ncol(M))) ### Train the Gaussian Naive Bayes gnb <- gaussian_naive_bayes(x = M, y = y) summary(gnb) # Classification head(predict(gnb, newdata = M, type = "class")) # head(gnb %class% M) # Posterior probabilities head(predict(gnb, newdata = M, type = "prob")) # head(gnb %prob% M) # Parameter estimates coef(gnb) ### Sparse data: train the Gaussian Naive Bayes library(Matrix) M_sparse <- Matrix(M, sparse = TRUE) class(M_sparse) # dgCMatrix # Fit the model with sparse data gnb_sparse <- gaussian_naive_bayes(M_sparse, y) # Classification head(predict(gnb_sparse, newdata = M_sparse, type = "class")) # Posterior probabilities head(predict(gnb_sparse, newdata = M_sparse, type = "prob")) # Parameter estimates coef(gnb_sparse) ### Equivalent calculation with general naive_bayes function. ### (no sparse data support by naive_bayes) nb <- naive_bayes(M, y) summary(nb) head(predict(nb, type = "prob")) # Obtain probability tables tables(nb, which = "V1") tables(gnb, which = "V1") # Visualise class conditional Gaussian distributions plot(nb, "V1", prob = "conditional") plot(gnb, which = "V1", prob = "conditional") # Check the equivalence of the class conditional distributions all(get_cond_dist(nb) == get_cond_dist(gnb))
Auxiliary function for "naive_bayes", "*_naive_bayes" and "naive_bayes_tables" objects for obtaining names of class conditional distributions assigned to the features.
get_cond_dist(object)get_cond_dist(object)
object |
object of class inheriting from |
vector with names of class conditional distributions assigned to the features.
Michal Majka, [email protected]
naive_bayes, bernoulli_naive_bayes, multinomial_naive_bayes, poisson_naive_bayes, gaussian_naive_bayes, tables
data(iris) nb <- naive_bayes(Species ~ ., data = iris) get_cond_dist(nb) # <=> attr(nb$tables, "cond_dist") get_cond_dist(tables(nb))data(iris) nb <- naive_bayes(Species ~ ., data = iris) get_cond_dist(nb) # <=> attr(nb$tables, "cond_dist") get_cond_dist(tables(nb))
The infix operators %class% and %prob% are shorthands for performing classification and obtaining posterior probabilities, respectively.
lhs %class% rhs lhs %prob% rhslhs %class% rhs lhs %prob% rhs
lhs |
object of class inheriting from |
rhs |
dataframe or matrix for "naive_bayes" objects OR matrix for all "*_naive_bayes" objects. |
If lhs is of class inheriting from the family of the Naive Bayes objects and rhs is either dataframe or matrix then the infix operators %class% and %prob% are equivalent to:
lhs %class% rhs <=> predict(lhs, newdata = rhs, type = "class", threshold = 0.001, eps = 0)
lhs %prob% rhs <=> predict(lhs, newdata = rhs, type == "prob", threshold = 0.001, eps = 0)
Compared to predict(), both operators do not allow changing values of fine tuning parameters threshold and eps.
%class% returns factor with class labels corresponding to the maximal conditional posterior probabilities.
%prob% returns a matrix with class label specific conditional posterior probabilities.
Michal Majka, [email protected]
predict.naive_bayes, predict.bernoulli_naive_bayes, predict.multinomial_naive_bayes, predict.poisson_naive_bayes, predict.gaussian_naive_bayes, predict.nonparametric_naive_bayes
### Fit the model nb <- naive_bayes(Species ~ ., iris) newdata <- iris[1:5,-5] # Let's pretend ### Classification nb %class% newdata predict(nb, newdata, type = "class") ### Posterior probabilities nb %prob% newdata predict(nb, newdata, type = "prob")### Fit the model nb <- naive_bayes(Species ~ ., iris) newdata <- iris[1:5,-5] # Let's pretend ### Classification nb %class% newdata predict(nb, newdata, type = "class") ### Posterior probabilities nb %prob% newdata predict(nb, newdata, type = "prob")
multinomial_naive_bayes is used to fit the Multinomial Naive Bayes model.
multinomial_naive_bayes(x, y, prior = NULL, laplace = 0.5, ...)multinomial_naive_bayes(x, y, prior = NULL, laplace = 0.5, ...)
x |
numeric matrix with integer predictors (matrix or dgCMatrix from Matrix package). |
y |
class vector (character/factor/logical). |
prior |
vector with prior probabilities of the classes. If unspecified, the class proportions for the training set are used. If present, the probabilities should be specified in the order of the factor levels. |
laplace |
value used for Laplace smoothing (additive smoothing). Defaults to 0.5. |
... |
not used. |
This is a specialized version of the Naive Bayes classifier, where the features represent frequencies generated by a multinomial distribution.
Sparse matrices of class "dgCMatrix" (Matrix package) are supported in order to speed up calculation times.
Please note that the Multinomial Naive Bayes is not available through the naive_bayes function.
multinomial_naive_bayes returns an object of class "multinomial_naive_bayes" which is a list with following components:
data |
list with two components: |
levels |
character vector with values of the class variable. |
laplace |
amount of Laplace smoothing (additive smoothing). |
params |
matrix with class conditional parameter estimates. |
prior |
numeric vector with prior probabilities. |
call |
the call that produced this object. |
Michal Majka, [email protected]
Manning, C.D., Raghavan, P., & Schütze, H. (2008). An Introduction to Information Retrieval. Cambridge: Cambridge University Press (Chapter 13). Available at https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf
predict.multinomial_naive_bayes, tables, get_cond_dist, %class%, coef.multinomial_naive_bayes
# library(naivebayes) ### Simulate the data: set.seed(1) cols <- 3 # words rows <- 10000 # all documents rows_spam <- 100 # spam documents prob_word_non_spam <- prop.table(runif(cols)) prob_word_spam <- prop.table(runif(cols)) M1 <- t(rmultinom(rows_spam, size = cols, prob = prob_word_spam)) M2 <- t(rmultinom(rows - rows_spam, size = cols, prob = prob_word_non_spam)) M <- rbind(M1, M2) colnames(M) <- paste0("word", 1:cols) ; rownames(M) <- paste0("doc", 1:rows) head(M) y <- c(rep("spam", rows_spam), rep("non-spam", rows - rows_spam)) ### Train the Multinomial Naive Bayes laplace <- 1 mnb <- multinomial_naive_bayes(x = M, y = y, laplace = laplace) summary(mnb) # Classification head(predict(mnb, newdata = M, type = "class")) # head(mnb %class% M) # Posterior probabilities head(predict(mnb, newdata = M, type = "prob")) # head(mnb %prob% M) # Parameter estimates coef(mnb) # Compare round(cbind(non_spam = prob_word_non_spam, spam = prob_word_spam), 3) ### Sparse data: train the Multinomial Naive Bayes library(Matrix) M_sparse <- Matrix(M, sparse = TRUE) class(M_sparse) # dgCMatrix # Fit the model with sparse data mnb_sparse <- multinomial_naive_bayes(M_sparse, y, laplace = laplace) # Classification head(predict(mnb_sparse, newdata = M_sparse, type = "class")) # Posterior probabilities head(predict(mnb_sparse, newdata = M_sparse, type = "prob")) # Parameter estimates coef(mnb_sparse)# library(naivebayes) ### Simulate the data: set.seed(1) cols <- 3 # words rows <- 10000 # all documents rows_spam <- 100 # spam documents prob_word_non_spam <- prop.table(runif(cols)) prob_word_spam <- prop.table(runif(cols)) M1 <- t(rmultinom(rows_spam, size = cols, prob = prob_word_spam)) M2 <- t(rmultinom(rows - rows_spam, size = cols, prob = prob_word_non_spam)) M <- rbind(M1, M2) colnames(M) <- paste0("word", 1:cols) ; rownames(M) <- paste0("doc", 1:rows) head(M) y <- c(rep("spam", rows_spam), rep("non-spam", rows - rows_spam)) ### Train the Multinomial Naive Bayes laplace <- 1 mnb <- multinomial_naive_bayes(x = M, y = y, laplace = laplace) summary(mnb) # Classification head(predict(mnb, newdata = M, type = "class")) # head(mnb %class% M) # Posterior probabilities head(predict(mnb, newdata = M, type = "prob")) # head(mnb %prob% M) # Parameter estimates coef(mnb) # Compare round(cbind(non_spam = prob_word_non_spam, spam = prob_word_spam), 3) ### Sparse data: train the Multinomial Naive Bayes library(Matrix) M_sparse <- Matrix(M, sparse = TRUE) class(M_sparse) # dgCMatrix # Fit the model with sparse data mnb_sparse <- multinomial_naive_bayes(M_sparse, y, laplace = laplace) # Classification head(predict(mnb_sparse, newdata = M_sparse, type = "class")) # Posterior probabilities head(predict(mnb_sparse, newdata = M_sparse, type = "prob")) # Parameter estimates coef(mnb_sparse)
naive_bayes is used to fit Naive Bayes model in which predictors are assumed to be independent within each class label.
## Default S3 method: naive_bayes(x, y, prior = NULL, laplace = 0, usekernel = FALSE, usepoisson = FALSE, ...) ## S3 method for class 'formula' naive_bayes(formula, data, prior = NULL, laplace = 0, usekernel = FALSE, usepoisson = FALSE, subset, na.action = stats::na.pass, ...)## Default S3 method: naive_bayes(x, y, prior = NULL, laplace = 0, usekernel = FALSE, usepoisson = FALSE, ...) ## S3 method for class 'formula' naive_bayes(formula, data, prior = NULL, laplace = 0, usekernel = FALSE, usepoisson = FALSE, subset, na.action = stats::na.pass, ...)
x |
matrix or dataframe with categorical (character/factor/logical) or metric (numeric) predictors. |
y |
class vector (character/factor/logical). |
formula |
an object of class |
data |
matrix or dataframe with categorical (character/factor/logical) or metric (numeric) predictors. |
prior |
vector with prior probabilities of the classes. If unspecified, the class proportions for the training set are used. If present, the probabilities should be specified in the order of the factor levels. |
laplace |
value used for Laplace smoothing (additive smoothing). Defaults to 0 (no Laplace smoothing). |
usekernel |
logical; if |
usepoisson |
logical; if |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
na.action |
a function which indicates what should happen when the data contain |
... |
other parameters to |
Numeric (metric) predictors are handled by assuming that they follow Gaussian distribution, given the class label. Alternatively, kernel density estimation can be used (usekernel = TRUE) to estimate their class-conditional distributions. Also, non-negative integer predictors (variables representing counts) can be modelled with Poisson distribution (usepoisson = TRUE); for further details please see Note below. Missing values are not included into constructing tables. Logical variables are treated as categorical (binary) variables.
naive_bayes returns an object of class "naive_bayes" which is a list with following components:
data |
list with two components: |
levels |
character vector with values of the class variable. |
laplace |
amount of Laplace smoothing (additive smoothing). |
tables |
list of tables. For each categorical predictor a table with class-conditional probabilities, for each integer predictor a table with Poisson mean (if |
prior |
numeric vector with prior probabilities. |
usekernel |
logical; |
usepoisson |
logical; |
call |
the call that produced this object. |
The class "numeric" contains "double" (double precision floating point numbers) and "integer". Depending on the parameters usekernel and usepoisson different class conditional distributions are applied to columns in the dataset with the class "numeric":
If usekernel=FALSE and poisson=FALSE then Gaussian distribution is applied to each "numeric" variable ("numeric"&"integer" or "numeric"&"double")
If usekernel=TRUE and poisson=FALSE then kernel density estimation (KDE) is applied to each "numeric" variable ("numeric"&"integer" or "numeric"&"double")
If usekernel=FALSE and poisson=TRUE then Gaussian distribution is applied to each "double" vector and Poisson to each "integer" vector. (Gaussian: "numeric" & "double"; Poisson: "numeric" & "integer")
If usekernel=TRUE and poisson=TRUE then kernel density estimation (KDE) is applied to each "double" vector and Poisson to each "integer" vector. (KDE: "numeric" & "double"; Poisson: "numeric" & "integer")
By default usekernel=FALSE and poisson=FALSE, thus Gaussian is applied to each numeric variable.
On the other hand, "character", "factor" and "logical" variables are assigned to the Categorical distribution with Bernoulli being its special case.
Prior the model fitting the classes of columns in the data.frame "data" can be easily checked via:
sapply(data, class)
sapply(data, is.numeric)
sapply(data, is.double)
sapply(data, is.integer)
Michal Majka, [email protected]
predict.naive_bayes, plot.naive_bayes, tables, get_cond_dist, %class%
### Simulate example data n <- 100 set.seed(1) data <- data.frame(class = sample(c("classA", "classB"), n, TRUE), bern = sample(LETTERS[1:2], n, TRUE), cat = sample(letters[1:3], n, TRUE), logical = sample(c(TRUE,FALSE), n, TRUE), norm = rnorm(n), count = rpois(n, lambda = c(5,15))) train <- data[1:95, ] test <- data[96:100, -1] ### 1) General usage via formula interface nb <- naive_bayes(class ~ ., train) summary(nb) # Classification predict(nb, test, type = "class") nb %class% test # Posterior probabilities predict(nb, test, type = "prob") nb %prob% test # Helper functions tables(nb, 1) get_cond_dist(nb) # Note: all "numeric" (integer, double) variables are modelled # with Gaussian distribution by default. ### 2) General usage via matrix/data.frame and class vector X <- train[-1] class <- train$class nb2 <- naive_bayes(x = X, y = class) nb2 %prob% test ### 3) Model continuous variables non-parametrically ### via kernel density estimation (KDE) nb_kde <- naive_bayes(class ~ ., train, usekernel = TRUE) summary(nb_kde) get_cond_dist(nb_kde) nb_kde %prob% test # Visualize class conditional densities plot(nb_kde, "norm", arg.num = list(legend.cex = 0.9), prob = "conditional") plot(nb_kde, "count", arg.num = list(legend.cex = 0.9), prob = "conditional") ### ?density and ?bw.nrd for further documentation # 3.1) Change Gaussian kernel to biweight kernel nb_kde_biweight <- naive_bayes(class ~ ., train, usekernel = TRUE, kernel = "biweight") nb_kde_biweight %prob% test plot(nb_kde_biweight, c("norm", "count"), arg.num = list(legend.cex = 0.9), prob = "conditional") # 3.2) Change "nrd0" (Silverman's rule of thumb) bandwidth selector nb_kde_SJ <- naive_bayes(class ~ ., train, usekernel = TRUE, bw = "SJ") nb_kde_SJ %prob% test plot(nb_kde_SJ, c("norm", "count"), arg.num = list(legend.cex = 0.9), prob = "conditional") # 3.3) Adjust bandwidth nb_kde_adjust <- naive_bayes(class ~ ., train, usekernel = TRUE, adjust = 1.5) nb_kde_adjust %prob% test plot(nb_kde_adjust, c("norm", "count"), arg.num = list(legend.cex = 0.9), prob = "conditional") ### 4) Model non-negative integers with Poisson distribution nb_pois <- naive_bayes(class ~ ., train, usekernel = TRUE, usepoisson = TRUE) summary(nb_pois) get_cond_dist(nb_pois) # Posterior probabilities nb_pois %prob% test # Class conditional distributions plot(nb_pois, "count", prob = "conditional") # Marginal distributions plot(nb_pois, "count", prob = "marginal") ## Not run: vars <- 10 rows <- 1000000 y <- sample(c("a", "b"), rows, TRUE) # Only categorical variables X1 <- as.data.frame(matrix(sample(letters[5:9], vars * rows, TRUE), ncol = vars)) nb_cat <- naive_bayes(x = X1, y = y) nb_cat system.time(pred2 <- predict(nb_cat, X1)) ## End(Not run)### Simulate example data n <- 100 set.seed(1) data <- data.frame(class = sample(c("classA", "classB"), n, TRUE), bern = sample(LETTERS[1:2], n, TRUE), cat = sample(letters[1:3], n, TRUE), logical = sample(c(TRUE,FALSE), n, TRUE), norm = rnorm(n), count = rpois(n, lambda = c(5,15))) train <- data[1:95, ] test <- data[96:100, -1] ### 1) General usage via formula interface nb <- naive_bayes(class ~ ., train) summary(nb) # Classification predict(nb, test, type = "class") nb %class% test # Posterior probabilities predict(nb, test, type = "prob") nb %prob% test # Helper functions tables(nb, 1) get_cond_dist(nb) # Note: all "numeric" (integer, double) variables are modelled # with Gaussian distribution by default. ### 2) General usage via matrix/data.frame and class vector X <- train[-1] class <- train$class nb2 <- naive_bayes(x = X, y = class) nb2 %prob% test ### 3) Model continuous variables non-parametrically ### via kernel density estimation (KDE) nb_kde <- naive_bayes(class ~ ., train, usekernel = TRUE) summary(nb_kde) get_cond_dist(nb_kde) nb_kde %prob% test # Visualize class conditional densities plot(nb_kde, "norm", arg.num = list(legend.cex = 0.9), prob = "conditional") plot(nb_kde, "count", arg.num = list(legend.cex = 0.9), prob = "conditional") ### ?density and ?bw.nrd for further documentation # 3.1) Change Gaussian kernel to biweight kernel nb_kde_biweight <- naive_bayes(class ~ ., train, usekernel = TRUE, kernel = "biweight") nb_kde_biweight %prob% test plot(nb_kde_biweight, c("norm", "count"), arg.num = list(legend.cex = 0.9), prob = "conditional") # 3.2) Change "nrd0" (Silverman's rule of thumb) bandwidth selector nb_kde_SJ <- naive_bayes(class ~ ., train, usekernel = TRUE, bw = "SJ") nb_kde_SJ %prob% test plot(nb_kde_SJ, c("norm", "count"), arg.num = list(legend.cex = 0.9), prob = "conditional") # 3.3) Adjust bandwidth nb_kde_adjust <- naive_bayes(class ~ ., train, usekernel = TRUE, adjust = 1.5) nb_kde_adjust %prob% test plot(nb_kde_adjust, c("norm", "count"), arg.num = list(legend.cex = 0.9), prob = "conditional") ### 4) Model non-negative integers with Poisson distribution nb_pois <- naive_bayes(class ~ ., train, usekernel = TRUE, usepoisson = TRUE) summary(nb_pois) get_cond_dist(nb_pois) # Posterior probabilities nb_pois %prob% test # Class conditional distributions plot(nb_pois, "count", prob = "conditional") # Marginal distributions plot(nb_pois, "count", prob = "marginal") ## Not run: vars <- 10 rows <- 1000000 y <- sample(c("a", "b"), rows, TRUE) # Only categorical variables X1 <- as.data.frame(matrix(sample(letters[5:9], vars * rows, TRUE), ncol = vars)) nb_cat <- naive_bayes(x = X1, y = y) nb_cat system.time(pred2 <- predict(nb_cat, X1)) ## End(Not run)
The naivebayes package presents an efficient implementation of the widely-used Naive Bayes classifier. It upholds three core principles: efficiency, user-friendliness, and reliance solely on Base R. By adhering to the latter principle, the package ensures stability and reliability without introducing external dependencies. This design choice maintains efficiency by leveraging the optimized routines inherent in Base R, many of which are programmed in high-performance languages like C/C++ or FORTRAN. By following these principles, the naivebayes package provides a reliable and efficient tool for Naive Bayes classification tasks, ensuring that users can perform their analyses effectively and with ease, even in the presence of missing data.
The general naive_bayes() function is designed to determine the class of each feature in a dataset, and depending on user specifications, it can assume various distributions for each feature. It currently supports the following class conditional distributions:
Categorical distribution for discrete features (with Bernoulli distribution as a special case for binary outcomes)
Poisson distribution for non-negative integer features
Gaussian distribution for continuous features
non-parametrically estimated densities via Kernel Density Estimation for continuous features
In addition to the general Naive Bayes function, the package provides specialized functions for various types of Naive Bayes classifiers. The specialized functions are carefully optimized for efficiency, utilizing linear algebra operations to excel when handling dense matrices. Additionally, they can also exploit sparsity of matrices for enhanced performance:
Bernoulli Naiive Bayes via bernoulli_naive_bayes()
Multinomial Naive Bayes via multinomial_naive_bayes()
Poisson Naive Bayes via poisson_naive_bayes()
Gaussian Naive Bayes via gaussian_naive_bayes()
Non-Parametric Naive Bayes via nonparametric_naive_bayes()
These specialized classifiers are tailored to different assumptions about the underlying data distributions, offering users versatile tools for classification tasks. Moreover, the package incorporates various helper functions aimed at enhancing the user experience. Notably, the model fitting functions provided by the package can effectively handle missing data, ensuring that users can utilize the classifiers even in the presence of incomplete information.
Extended documentation can be found on the website:
Bug reports:
Contact:
nonparametric_naive_bayes is used to fit the Non-Parametric Naive Bayes model in which all class conditional distributions are non-parametrically estimated using kernel density estimator and are assumed to be independent.
nonparametric_naive_bayes(x, y, prior = NULL, ...)nonparametric_naive_bayes(x, y, prior = NULL, ...)
x |
matrix with metric predictors (only numeric matrix accepted). |
y |
class vector (character/factor/logical). |
prior |
vector with prior probabilities of the classes. If unspecified, the class proportions for the training set are used. If present, the probabilities should be specified in the order of the factor levels. |
... |
other parameters to |
This is a specialized version of the Naive Bayes classifier, in which all features take on real values (numeric/integer) and class conditional probabilities are estimated in a non-parametric way with the kernel density estimator (KDE). By default Gaussian kernel is used and the smoothing bandwidth is selected according to the Silverman's 'rule of thumb'. For more details, please see the references and the documentation of density and bw.nrd0.
The Non-Parametric Naive Bayes is available in both, naive_bayes() and nonparametric_naive_bayes(). The latter does not provide a substantial speed up over the general naive_bayes() function but it is meant to be more transparent and user friendly.
The nonparametric_naive_bayes and naive_bayes() are equivalent when the latter is used with usekernel = TRUE and usepoisson = FALSE; and a matrix/data.frame contains only numeric variables.
The missing values (NAs) are omitted during the estimation process. Also, the corresponding predict function excludes all NAs from the calculation of posterior probabilities (an informative warning is always given).
nonparametric_naive_bayes returns an object of class "nonparametric_naive_bayes" which is a list with following components:
data |
list with two components: |
levels |
character vector with values of the class variable. |
dens |
nested list containing |
prior |
numeric vector with prior probabilities. |
call |
the call that produced this object. |
Michal Majka, [email protected]
Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall.
predict.nonparametric_naive_bayes, plot.nonparametric_naive_bayes, tables, get_cond_dist, %class%
# library(naivebayes) data(iris) y <- iris[[5]] M <- as.matrix(iris[-5]) ### Train the Non-Parametric Naive Bayes nnb <- nonparametric_naive_bayes(x = M, y = y) summary(nnb) head(predict(nnb, newdata = M, type = "prob")) ### Equivalent calculation with general naive_bayes function: nb <- naive_bayes(M, y, usekernel = TRUE) summary(nb) head(predict(nb, type = "prob")) ### Change kernel nnb_kernel <- nonparametric_naive_bayes(x = M, y = y, kernel = "biweight") plot(nnb_kernel, 1, prob = "conditional") ### Adjust bandwidth nnb_adjust <- nonparametric_naive_bayes(M, y, adjust = 1.5) plot(nnb_adjust, 1, prob = "conditional") ### Change bandwidth selector nnb_bw <- nonparametric_naive_bayes(M, y, bw = "SJ") plot(nnb_bw, 1, prob = "conditional") ### Obtain tables with conditional densities # tables(nnb, which = 1)# library(naivebayes) data(iris) y <- iris[[5]] M <- as.matrix(iris[-5]) ### Train the Non-Parametric Naive Bayes nnb <- nonparametric_naive_bayes(x = M, y = y) summary(nnb) head(predict(nnb, newdata = M, type = "prob")) ### Equivalent calculation with general naive_bayes function: nb <- naive_bayes(M, y, usekernel = TRUE) summary(nb) head(predict(nb, type = "prob")) ### Change kernel nnb_kernel <- nonparametric_naive_bayes(x = M, y = y, kernel = "biweight") plot(nnb_kernel, 1, prob = "conditional") ### Adjust bandwidth nnb_adjust <- nonparametric_naive_bayes(M, y, adjust = 1.5) plot(nnb_adjust, 1, prob = "conditional") ### Change bandwidth selector nnb_bw <- nonparametric_naive_bayes(M, y, bw = "SJ") plot(nnb_bw, 1, prob = "conditional") ### Obtain tables with conditional densities # tables(nnb, which = 1)
Plot method for objects of class "bernoulli_naive_bayes" designed for a quick look at the class marginal distributions or class conditional distributions of 0-1 valued predictors.
## S3 method for class 'bernoulli_naive_bayes' plot(x, which = NULL, ask = FALSE, arg.cat = list(), prob = c("marginal", "conditional"), ...)## S3 method for class 'bernoulli_naive_bayes' plot(x, which = NULL, ask = FALSE, arg.cat = list(), prob = c("marginal", "conditional"), ...)
x |
object of class inheriting from |
which |
variables to be plotted (all by default). This can be any valid indexing vector or vector containing names of variables. |
ask |
logical; if |
arg.cat |
other parameters to be passed as a named list to |
prob |
character; if "marginal" then marginal distributions of predictor variables for each class are visualised and if "conditional" then the class conditional distributions of predictor variables are depicted. By default, prob="marginal". |
... |
not used. |
Class conditional or class conditional distributions are visualised by mosaicplot.
The parameter prob controls the kind of probabilities to be visualized for each individual predictor . It can take on two values:
"marginal":
"conditional":
Michal Majka, [email protected]
naive_bayes,bernoulli_naive_bayes predict.bernoulli_naive_bayes, tables, get_cond_dist
# Simulate data cols <- 10 ; rows <- 100 ; probs <- c("0" = 0.4, "1" = 0.1) M <- matrix(sample(0:1, rows * cols, TRUE, probs), nrow = rows, ncol = cols) y <- factor(sample(paste0("class", LETTERS[1:2]), rows, TRUE, prob = c(0.3,0.7))) colnames(M) <- paste0("V", seq_len(ncol(M))) laplace <- 0.5 # Train the Bernoulli Naive Bayes model bnb <- bernoulli_naive_bayes(x = M, y = y, laplace = laplace) # Visualize class marginal probabilities corresponding to the first feature plot(bnb, which = 1) # Visualize class conditional probabilities corresponding to the first feature plot(bnb, which = 1, prob = "conditional")# Simulate data cols <- 10 ; rows <- 100 ; probs <- c("0" = 0.4, "1" = 0.1) M <- matrix(sample(0:1, rows * cols, TRUE, probs), nrow = rows, ncol = cols) y <- factor(sample(paste0("class", LETTERS[1:2]), rows, TRUE, prob = c(0.3,0.7))) colnames(M) <- paste0("V", seq_len(ncol(M))) laplace <- 0.5 # Train the Bernoulli Naive Bayes model bnb <- bernoulli_naive_bayes(x = M, y = y, laplace = laplace) # Visualize class marginal probabilities corresponding to the first feature plot(bnb, which = 1) # Visualize class conditional probabilities corresponding to the first feature plot(bnb, which = 1, prob = "conditional")
Plot method for objects of class "gaussian_naive_bayes" designed for a quick look at the class marginal or conditional Gaussian distributions of metric predictors.
## S3 method for class 'gaussian_naive_bayes' plot(x, which = NULL, ask = FALSE, legend = TRUE, legend.box = FALSE, arg.num = list(), prob = c("marginal", "conditional"), ...)## S3 method for class 'gaussian_naive_bayes' plot(x, which = NULL, ask = FALSE, legend = TRUE, legend.box = FALSE, arg.num = list(), prob = c("marginal", "conditional"), ...)
x |
object of class inheriting from |
which |
variables to be plotted (all by default). This can be any valid indexing vector or vector containing names of variables. |
ask |
logical; if |
legend |
logical; if |
legend.box |
logical; if |
arg.num |
other parameters to be passed as a named list to |
prob |
character; if "marginal" then marginal distributions of predictor variables for each class are visualised and if "conditional" then the class conditional distributions of predictor variables are depicted. By default, prob="marginal". |
... |
not used. |
Class marginal and class conditional Gaussian distributions are visualised by matplot.
The parameter prob controls the kind of probabilities to be visualized for each individual predictor . It can take on two values:
"marginal":
"conditional":
Michal Majka, [email protected]
naive_bayes, gaussian_naive_bayes, predict.gaussian_naive_bayes, tables, get_cond_dist
data(iris) y <- iris[[5]] M <- as.matrix(iris[-5]) ### Train the Gaussian Naive Bayes with custom prior gnb <- gaussian_naive_bayes(x = M, y = y, prior = c(0.1,0.3,0.6)) # Visualize class marginal Gaussian distributions corresponding # to the first feature plot(gnb, which = 1) # Visualize class conditional Gaussian distributions corresponding # to the first feature plot(gnb, which = 1, prob = "conditional")data(iris) y <- iris[[5]] M <- as.matrix(iris[-5]) ### Train the Gaussian Naive Bayes with custom prior gnb <- gaussian_naive_bayes(x = M, y = y, prior = c(0.1,0.3,0.6)) # Visualize class marginal Gaussian distributions corresponding # to the first feature plot(gnb, which = 1) # Visualize class conditional Gaussian distributions corresponding # to the first feature plot(gnb, which = 1, prob = "conditional")
Plot method for objects of class "naive_bayes" designed for a quick look at the class marginal distributions or class conditional distributions of predictor variables.
## S3 method for class 'naive_bayes' plot(x, which = NULL, ask = FALSE, legend = TRUE, legend.box = FALSE, arg.num = list(), arg.cat = list(), prob = c("marginal", "conditional"), ...)## S3 method for class 'naive_bayes' plot(x, which = NULL, ask = FALSE, legend = TRUE, legend.box = FALSE, arg.num = list(), arg.cat = list(), prob = c("marginal", "conditional"), ...)
x |
object of class inheriting from |
which |
variables to be plotted (all by default). This can be any valid indexing vector or vector containing names of variables. |
ask |
logical; if |
legend |
logical; if |
legend.box |
logical; if |
arg.num |
other parameters to be passed as a named list to |
arg.cat |
other parameters to be passed as a named list to |
prob |
character; if "marginal" then marginal distributions of predictor variables for each class are visualised and if "conditional" then the class conditional distributions of predictor variables are depicted. By default, prob="marginal". |
... |
not used. |
Probabilities are visualised by matplot (for numeric (metric) predictors) and mosaicplot (for categorical predictors). In case of non parametric estimation of densities, the bandwidths are reported for each class. Nothing is returned. For numeric (metric) predictors position of the legend can be adjusted changed via arg.num(..., legend.position = "topright"). legend.position can be one of "topright" "topleft", "bottomright", "bottomleft". In order to adjust the legend size following argument can be used: arg.num(..., legend.cex = 0.9).
The parameter prob controls the kind of probabilities to be visualized for each individual predictor . It can take on two values:
"marginal":
"conditional":
Michal Majka, [email protected]
naive_bayes, predict.naive_bayes, %class%, tables, get_cond_dist
data(iris) iris2 <- cbind(iris, New = sample(letters[1:3], 150, TRUE)) # Fit the model with custom prior probabilities nb <- naive_bayes(Species ~ ., data = iris2, prior = c(0.1, 0.3, 0.6)) # Visualize marginal distributions of two predictors plot(nb, which = c("Sepal.Width", "Sepal.Length"), ask = TRUE) # Visualize class conditional distributions corresponding to the first predictor # with customized settings plot(nb, which = 1, ask = FALSE, prob = "conditional", arg.num = list(col = 1:3, lty = 1, main = "Naive Bayes Plot", legend.position = "topright", legend.cex = 0.55)) # Visualize class marginal distributions corresponding to the first predictor # with customized settings plot(nb, which = 1, ask = FALSE, prob = "marginal", arg.num = list(col = 1:3, lty = 1, main = "Naive Bayes Plot", legend.position = "topright", legend.cex = 0.55)) # Visualize class marginal distribution corresponding to the predictor "new" # with custom colours plot(nb, which = "New", arg.cat = list(color = gray.colors(3)))data(iris) iris2 <- cbind(iris, New = sample(letters[1:3], 150, TRUE)) # Fit the model with custom prior probabilities nb <- naive_bayes(Species ~ ., data = iris2, prior = c(0.1, 0.3, 0.6)) # Visualize marginal distributions of two predictors plot(nb, which = c("Sepal.Width", "Sepal.Length"), ask = TRUE) # Visualize class conditional distributions corresponding to the first predictor # with customized settings plot(nb, which = 1, ask = FALSE, prob = "conditional", arg.num = list(col = 1:3, lty = 1, main = "Naive Bayes Plot", legend.position = "topright", legend.cex = 0.55)) # Visualize class marginal distributions corresponding to the first predictor # with customized settings plot(nb, which = 1, ask = FALSE, prob = "marginal", arg.num = list(col = 1:3, lty = 1, main = "Naive Bayes Plot", legend.position = "topright", legend.cex = 0.55)) # Visualize class marginal distribution corresponding to the predictor "new" # with custom colours plot(nb, which = "New", arg.cat = list(color = gray.colors(3)))
Plot method for objects of class "nonparametric_naive_bayes" designed for a quick look at the estimated class marginal or class conditional densities of metric predictors.
## S3 method for class 'nonparametric_naive_bayes' plot(x, which = NULL, ask = FALSE, legend = TRUE, legend.box = FALSE, arg.num = list(), prob = c("marginal", "conditional"), ...)## S3 method for class 'nonparametric_naive_bayes' plot(x, which = NULL, ask = FALSE, legend = TRUE, legend.box = FALSE, arg.num = list(), prob = c("marginal", "conditional"), ...)
x |
object of class inheriting from |
which |
variables to be plotted (all by default). This can be any valid indexing vector or vector containing names of variables. |
ask |
logical; if |
legend |
logical; if |
legend.box |
logical; if |
arg.num |
other parameters to be passed as a named list to |
prob |
character; if "marginal" then marginal distributions of predictor variables for each class are visualised and if "conditional" then the class conditional distributions of predictor variables are depicted. By default, prob="marginal". |
... |
not used. |
Estimated class marginal or class conditional densities are visualised by matplot.
The parameter prob controls the kind of probabilities to be visualized for each individual predictor . It can take on two values:
"marginal":
"conditional":
Michal Majka, [email protected]
naive_bayes, nonparametric_naive_bayes predict.nonparametric_naive_bayes, tables, get_cond_dist
data(iris) y <- iris[[5]] M <- as.matrix(iris[-5]) ### Train the Non-Parametric Naive Bayes with custom prior prior <- c(0.1,0.3,0.6) nnb <- nonparametric_naive_bayes(x = M, y = y, prior = prior) nnb2 <- nonparametric_naive_bayes(x = M, y = y, prior = prior, adjust = 1.5) nnb3 <- nonparametric_naive_bayes(x = M, y = y, prior = prior, bw = "ucv") # Visualize estimated class conditional densities corresponding # to the first feature plot(nnb, which = 1, prob = "conditional") plot(nnb2, which = 1, prob = "cond") plot(nnb3, which = 1, prob = "c") # Visualize estimated class marginal densities corresponding # to the first feature plot(nnb, which = 1) plot(nnb2, which = 1) plot(nnb3, which = 1)data(iris) y <- iris[[5]] M <- as.matrix(iris[-5]) ### Train the Non-Parametric Naive Bayes with custom prior prior <- c(0.1,0.3,0.6) nnb <- nonparametric_naive_bayes(x = M, y = y, prior = prior) nnb2 <- nonparametric_naive_bayes(x = M, y = y, prior = prior, adjust = 1.5) nnb3 <- nonparametric_naive_bayes(x = M, y = y, prior = prior, bw = "ucv") # Visualize estimated class conditional densities corresponding # to the first feature plot(nnb, which = 1, prob = "conditional") plot(nnb2, which = 1, prob = "cond") plot(nnb3, which = 1, prob = "c") # Visualize estimated class marginal densities corresponding # to the first feature plot(nnb, which = 1) plot(nnb2, which = 1) plot(nnb3, which = 1)
Plot method for objects of class "poisson_naive_bayes" designed for a quick look at the class marginal or class conditional Poisson distributions of non-negative integer predictors.
## S3 method for class 'poisson_naive_bayes' plot(x, which = NULL, ask = FALSE, legend = TRUE, legend.box = FALSE, arg.num = list(), prob = c("marginal", "conditional"), ...)## S3 method for class 'poisson_naive_bayes' plot(x, which = NULL, ask = FALSE, legend = TRUE, legend.box = FALSE, arg.num = list(), prob = c("marginal", "conditional"), ...)
x |
object of class inheriting from |
which |
variables to be plotted (all by default). This can be any valid indexing vector or vector containing names of variables. |
ask |
logical; if |
legend |
logical; if |
legend.box |
logical; if |
arg.num |
other parameters to be passed as a named list to |
prob |
character; if "marginal" then marginal distributions of predictor variables for each class are visualised and if "conditional" then the class conditional distributions of predictor variables are depicted. By default, prob="marginal". |
... |
not used. |
Class marginal or class conditional Poisson distributions are visualised by matplot.
The parameter prob controls the kind of probabilities to be visualized for each individual predictor . It can take on two values:
"marginal":
"conditional":
Michal Majka, [email protected]
naive_bayes, poisson_naive_bayes, predict.poisson_naive_bayes, tables, get_cond_dist
cols <- 10 ; rows <- 100 M <- matrix(rpois(rows * cols, lambda = 3), nrow = rows, ncol = cols) # is.integer(M) # [1] TRUE y <- factor(sample(paste0("class", LETTERS[1:2]), rows, TRUE)) colnames(M) <- paste0("V", seq_len(ncol(M))) laplace <- 0 ### Train the Poisson Naive Bayes pnb <- poisson_naive_bayes(x = M, y = y, laplace = laplace) # Visualize class conditional Poisson distributions corresponding # to the first feature plot(pnb, which = 1, prob = "conditional") # Visualize class marginal Poisson distributions corresponding # to the first feature plot(pnb, which = 1)cols <- 10 ; rows <- 100 M <- matrix(rpois(rows * cols, lambda = 3), nrow = rows, ncol = cols) # is.integer(M) # [1] TRUE y <- factor(sample(paste0("class", LETTERS[1:2]), rows, TRUE)) colnames(M) <- paste0("V", seq_len(ncol(M))) laplace <- 0 ### Train the Poisson Naive Bayes pnb <- poisson_naive_bayes(x = M, y = y, laplace = laplace) # Visualize class conditional Poisson distributions corresponding # to the first feature plot(pnb, which = 1, prob = "conditional") # Visualize class marginal Poisson distributions corresponding # to the first feature plot(pnb, which = 1)
poisson_naive_bayes is used to fit the Poisson Naive Bayes model in which all class conditional distributions are assumed to be Poisson and be independent.
poisson_naive_bayes(x, y, prior = NULL, laplace = 0, ...)poisson_naive_bayes(x, y, prior = NULL, laplace = 0, ...)
x |
numeric matrix with integer predictors (matrix or dgCMatrix from Matrix package). |
y |
class vector (character/factor/logical). |
prior |
vector with prior probabilities of the classes. If unspecified, the class proportions for the training set are used. If present, the probabilities should be specified in the order of the factor levels. |
laplace |
value used for Laplace smoothing (additive smoothing). Defaults to 0 (no Laplace smoothing). |
... |
not used. |
This is a specialized version of the Naive Bayes classifier, in which all features take on non-negative integers (numeric/integer) and class conditional probabilities are modelled with the Poisson distribution.
The Poisson Naive Bayes is available in both, naive_bayes and poisson_naive_bayes. The latter provides more efficient performance though. Faster calculation times come from restricting the data to an integer-valued matrix and taking advantage of linear algebra operations. Sparse matrices of class "dgCMatrix" (Matrix package) are supported in order to furthermore speed up calculation times.
The poisson_naive_bayes and naive_bayes() are equivalent when the latter is used with usepoisson = TRUE and usekernel = FALSE; and a matrix/data.frame contains only integer-valued columns.
The missing values (NAs) are omited during the estimation process. Also, the corresponding predict function excludes all NAs from the calculation of posterior probabilities (an informative warning is always given).
poisson_naive_bayes returns an object of class "poisson_naive_bayes" which is a list with following components:
data |
list with two components: |
levels |
character vector with values of the class variable. |
laplace |
amount of Laplace smoothing (additive smoothing). |
params |
matrix containing class conditional means. |
prior |
numeric vector with prior probabilities. |
call |
the call that produced this object. |
When the parameter laplace is set to positive constant c then this amount is added to all counts. This leads to the ("global") Bayesian estimation with an improper prior. In each case, the estimate is the expected value of the posterior which is given by the gamma distribution with parameters: cell count + c and number of observations in the cell.
If in one cell there is a zero count and laplace = 0 then one pseudo-count is automatically to each such cell. This corresponds to the "local" Bayesian estimation with uniform prior.
Michal Majka, [email protected]
predict.poisson_naive_bayes, plot.poisson_naive_bayes, tables, get_cond_dist, %class%, coef.poisson_naive_bayes
library(naivebayes) ### Simulate the data: set.seed(1) cols <- 10 ; rows <- 100 M <- matrix(rpois(rows * cols, lambda = 3), nrow = rows, ncol = cols) y <- factor(sample(paste0("class", LETTERS[1:2]), rows, TRUE, prob = c(0.3,0.7))) colnames(M) <- paste0("V", seq_len(ncol(M))) laplace <- 0.5 ### Train the Poisson Naive Bayes pnb <- poisson_naive_bayes(x = M, y = y, laplace = laplace) summary(pnb) # Classification head(predict(pnb, newdata = M, type = "class")) # head(pnb %class% M) # Posterior probabilities head(predict(pnb, newdata = M, type = "prob")) # head(pnb %prob% M) # Parameter estimates coef(pnb) ### Sparse data: train the Poisson Naive Bayes library(Matrix) M_sparse <- Matrix(M, sparse = TRUE) class(M_sparse) # dgCMatrix # Fit the model with sparse data pnb_sparse <- poisson_naive_bayes(M_sparse, y, laplace = laplace) # Classification head(predict(pnb_sparse, newdata = M_sparse, type = "class")) # Posterior probabilities head(predict(pnb_sparse, newdata = M_sparse, type = "prob")) # Parameter estimates coef(pnb_sparse) ### Equivalent calculation with general naive_bayes function. ### (no sparse data support by naive_bayes) nb <- naive_bayes(M, y, laplace = laplace, usepoisson = TRUE) summary(nb) head(predict(nb, type = "prob")) # Obtain probability tables tables(nb, which = "V1") tables(pnb, which = "V1") # Visualise class conditional Poisson distributions plot(nb, "V1", prob = "conditional") plot(pnb, which = "V1", prob = "conditional") # Check the equivalence of the class conditional distributions all(get_cond_dist(nb) == get_cond_dist(pnb))library(naivebayes) ### Simulate the data: set.seed(1) cols <- 10 ; rows <- 100 M <- matrix(rpois(rows * cols, lambda = 3), nrow = rows, ncol = cols) y <- factor(sample(paste0("class", LETTERS[1:2]), rows, TRUE, prob = c(0.3,0.7))) colnames(M) <- paste0("V", seq_len(ncol(M))) laplace <- 0.5 ### Train the Poisson Naive Bayes pnb <- poisson_naive_bayes(x = M, y = y, laplace = laplace) summary(pnb) # Classification head(predict(pnb, newdata = M, type = "class")) # head(pnb %class% M) # Posterior probabilities head(predict(pnb, newdata = M, type = "prob")) # head(pnb %prob% M) # Parameter estimates coef(pnb) ### Sparse data: train the Poisson Naive Bayes library(Matrix) M_sparse <- Matrix(M, sparse = TRUE) class(M_sparse) # dgCMatrix # Fit the model with sparse data pnb_sparse <- poisson_naive_bayes(M_sparse, y, laplace = laplace) # Classification head(predict(pnb_sparse, newdata = M_sparse, type = "class")) # Posterior probabilities head(predict(pnb_sparse, newdata = M_sparse, type = "prob")) # Parameter estimates coef(pnb_sparse) ### Equivalent calculation with general naive_bayes function. ### (no sparse data support by naive_bayes) nb <- naive_bayes(M, y, laplace = laplace, usepoisson = TRUE) summary(nb) head(predict(nb, type = "prob")) # Obtain probability tables tables(nb, which = "V1") tables(pnb, which = "V1") # Visualise class conditional Poisson distributions plot(nb, "V1", prob = "conditional") plot(pnb, which = "V1", prob = "conditional") # Check the equivalence of the class conditional distributions all(get_cond_dist(nb) == get_cond_dist(pnb))
Classification based on the Bernoulli Naive Bayes model.
## S3 method for class 'bernoulli_naive_bayes' predict(object, newdata = NULL, type = c("class","prob"), ...)## S3 method for class 'bernoulli_naive_bayes' predict(object, newdata = NULL, type = c("class","prob"), ...)
object |
object of class inheriting from |
newdata |
matrix with numeric 0-1 predictors. |
type |
if "class", new data points are classified according to the highest posterior probabilities. If "prob", the posterior probabilities for each class are returned. |
... |
not used. |
This is a specialized version of the Naive Bayes classifier, in which all features take on numeric 0-1 values and class conditional probabilities are modelled with the Bernoulli distribution.
Class posterior probabilities are calculated using the Bayes' rule under the assumption of independence of predictors. If no newdata is provided, the data from the object is used.
The Bernoulli Naive Bayes is available in both, naive_bayes and bernoulli_naive_bayes. The implementation of the specialized Naive Bayes provides more efficient performance though. The speedup comes from the restricting the data input to a numeric 0-1 matrix and performing the linear algebra as well as vectorized operations on it. In other words, the efficiency comes at cost of the flexibility.
The NAs in the newdata are not included into the calculation of posterior probabilities; and if present an informative warning is given.
The bernoulli_naive_bayes function is equivalent to the naive_bayes function with the numeric 0-1 matrix being coerced, for instance, to the "0"-"1" character matrix.
predict.bernoulli_naive_bayes returns either a factor with class labels corresponding to the maximal conditional posterior probabilities or a matrix with class label specific conditional posterior probabilities.
Michal Majka, [email protected]
naive_bayes, bernoulli_naive_bayes, plot.bernoulli_naive_bayes, tables, get_cond_dist, %class%
cols <- 10 ; rows <- 100 ; probs <- c("0" = 0.4, "1" = 0.1) M <- matrix(sample(0:1, rows * cols, TRUE, probs), nrow = rows, ncol = cols) y <- factor(sample(paste0("class", LETTERS[1:2]), rows, TRUE, prob = c(0.3,0.7))) colnames(M) <- paste0("V", seq_len(ncol(M))) laplace <- 0.5 ### Train the Bernoulli Naive Bayes bnb <- bernoulli_naive_bayes(x = M, y = y, laplace = laplace) ### Classification head(predict(bnb, newdata = M, type = "class")) head(bnb %class% M) ### Posterior probabilities head(predict(bnb, newdata = M, type = "prob")) head(bnb %prob% M)cols <- 10 ; rows <- 100 ; probs <- c("0" = 0.4, "1" = 0.1) M <- matrix(sample(0:1, rows * cols, TRUE, probs), nrow = rows, ncol = cols) y <- factor(sample(paste0("class", LETTERS[1:2]), rows, TRUE, prob = c(0.3,0.7))) colnames(M) <- paste0("V", seq_len(ncol(M))) laplace <- 0.5 ### Train the Bernoulli Naive Bayes bnb <- bernoulli_naive_bayes(x = M, y = y, laplace = laplace) ### Classification head(predict(bnb, newdata = M, type = "class")) head(bnb %class% M) ### Posterior probabilities head(predict(bnb, newdata = M, type = "prob")) head(bnb %prob% M)
Classification based on the Gaussian Naive Bayes model.
## S3 method for class 'gaussian_naive_bayes' predict(object, newdata = NULL, type = c("class","prob"), threshold = 0.001, eps = 0, ...)## S3 method for class 'gaussian_naive_bayes' predict(object, newdata = NULL, type = c("class","prob"), threshold = 0.001, eps = 0, ...)
object |
object of class inheriting from |
newdata |
matrix with metric predictors (only numeric matrix accepted). |
type |
if "class", new data points are classified according to the highest posterior probabilities. If "prob", the posterior probabilities for each class are returned. |
threshold |
value by which zero probabilities or probabilities within the epsilon-range corresponding to metric variables are replaced (zero probabilities corresponding to categorical variables can be handled with Laplace (additive) smoothing). |
eps |
value that specifies an epsilon-range to replace zero or close to zero probabilities by |
... |
not used. |
This is a specialized version of the Naive Bayes classifier, in which all features take on real values and class conditional probabilities are modelled with the Gaussian distribution.
Class posterior probabilities are calculated using the Bayes' rule under the assumption of independence of predictors. If no newdata is provided, the data from the object is used.
The Gaussian Naive Bayes is available in both, naive_bayes and gaussian_naive_bayes. The implementation of the specialized Naive Bayes provides more efficient performance though. The speedup comes from the restricting the data input to a numeric matrix and performing the linear algebra as well vectorized operations on it. In other words, the efficiency comes at cost of the flexibility.
The NAs in the newdata are not included into the calculation of posterior probabilities; and if present an informative warning is given.
The gaussian_naive_bayes function is equivalent to the naive_bayes function with the numeric matrix or a data.frame containing only numeric variables.
predict.gaussian_naive_bayes returns either a factor with class labels corresponding to the maximal conditional posterior probabilities or a matrix with class label specific conditional posterior probabilities.
Michal Majka, [email protected]
naive_bayes, gaussian_naive_bayes, plot.gaussian_naive_bayes, tables, get_cond_dist, %class%
data(iris) y <- iris[[5]] M <- as.matrix(iris[-5]) ### Train the Gaussian Naive Bayes gnb <- gaussian_naive_bayes(x = M, y = y) ### Classification head(predict(gnb, newdata = M, type = "class")) head(gnb %class% M) ### Posterior probabilities head(predict(gnb, newdata = M, type = "prob")) head(gnb %prob% M)data(iris) y <- iris[[5]] M <- as.matrix(iris[-5]) ### Train the Gaussian Naive Bayes gnb <- gaussian_naive_bayes(x = M, y = y) ### Classification head(predict(gnb, newdata = M, type = "class")) head(gnb %class% M) ### Posterior probabilities head(predict(gnb, newdata = M, type = "prob")) head(gnb %prob% M)
Classification based on the Multinomial Naive Bayes model.
## S3 method for class 'multinomial_naive_bayes' predict(object, newdata = NULL, type = c("class","prob"), ...)## S3 method for class 'multinomial_naive_bayes' predict(object, newdata = NULL, type = c("class","prob"), ...)
object |
object of class inheriting from |
newdata |
matrix with non-negative integer predictors (only numeric matrix is accepted). |
type |
if "class", new data points are classified according to the highest posterior probabilities. If "prob", the posterior probabilities for each class are returned. |
... |
not used. |
This is a specialized version of the Naive Bayes classifier, where the features represent the frequencies with which events have been generated by a multinomial distribution.
The Multinomial Naive Bayes is not available through the naive_bayes function.
The NAs in the newdata are not included into the calculation of posterior probabilities; and if present an informative warning is given.
predict.multinomial_naive_bayes returns either a factor with class labels corresponding to the maximal conditional posterior probabilities or a matrix with class label specific conditional posterior probabilities.
Michal Majka, [email protected]
McCallum, Andrew; Nigam, Kamal (1998). A comparison of event models for Naive Bayes text classification (PDF). AAAI-98 workshop on learning for text categorization. 752. http://www.cs.cmu.edu/~knigam/papers/multinomial-aaaiws98.pdf
multinomial_naive_bayes, tables, get_cond_dist, %class%, coef.multinomial_naive_bayes
### Simulate the data: cols <- 10 ; rows <- 100 M <- matrix(sample(0:5, rows * cols, TRUE), nrow = rows, ncol = cols) y <- factor(sample(paste0("class", LETTERS[1:2]), rows, TRUE, prob = c(0.3,0.7))) colnames(M) <- paste0("V", seq_len(ncol(M))) laplace <- 1 ### Train the Multinomial Naive Bayes mnb <- multinomial_naive_bayes(x = M, y = y, laplace = laplace) # Classification head(predict(mnb, newdata = M, type = "class")) head(mnb %class% M) # Posterior probabilities head(predict(mnb, newdata = M, type = "prob")) head(mnb %prob% M)### Simulate the data: cols <- 10 ; rows <- 100 M <- matrix(sample(0:5, rows * cols, TRUE), nrow = rows, ncol = cols) y <- factor(sample(paste0("class", LETTERS[1:2]), rows, TRUE, prob = c(0.3,0.7))) colnames(M) <- paste0("V", seq_len(ncol(M))) laplace <- 1 ### Train the Multinomial Naive Bayes mnb <- multinomial_naive_bayes(x = M, y = y, laplace = laplace) # Classification head(predict(mnb, newdata = M, type = "class")) head(mnb %class% M) # Posterior probabilities head(predict(mnb, newdata = M, type = "prob")) head(mnb %prob% M)
Classification based on Naive Bayes models.
## S3 method for class 'naive_bayes' predict(object, newdata = NULL, type = c("class","prob"), threshold = 0.001, eps = 0, ...)## S3 method for class 'naive_bayes' predict(object, newdata = NULL, type = c("class","prob"), threshold = 0.001, eps = 0, ...)
object |
object of class inheriting from |
newdata |
matrix or dataframe with categorical (character/factor/logical) or metric (numeric) predictors. |
type |
if "class", new data points are classified according to the highest posterior probabilities. If "prob", the posterior probabilities for each class are returned. |
threshold |
value by which zero probabilities or probabilities within the epsilon-range corresponding to metric variables are replaced (zero probabilities corresponding to categorical variables can be handled with Laplace (additive) smoothing). |
eps |
value that specifies an epsilon-range to replace zero or close to zero probabilities by |
... |
not used. |
Computes conditional posterior probabilities for each class label using the Bayes' rule under the assumption of independence of predictors. If no new data is provided, the data from the object is used. Logical variables are treated as categorical (binary) variables. Predictors with missing values are not included into the computation of posterior probabilities.
predict.naive_bayes returns either a factor with class labels corresponding to the maximal conditional posterior probabilities or a matrix with class label specific conditional posterior probabilities.
Michal Majka, [email protected]
naive_bayes, plot.naive_bayes, tables, get_cond_dist, %class%
### Simulate example data n <- 100 set.seed(1) data <- data.frame(class = sample(c("classA", "classB"), n, TRUE), bern = sample(LETTERS[1:2], n, TRUE), cat = sample(letters[1:3], n, TRUE), logical = sample(c(TRUE,FALSE), n, TRUE), norm = rnorm(n), count = rpois(n, lambda = c(5,15))) train <- data[1:95, ] test <- data[96:100, -1] ### Fit the model with default settings nb <- naive_bayes(class ~ ., train) # Classification predict(nb, test, type = "class") nb %class% test # Posterior probabilities predict(nb, test, type = "prob") nb %prob% test ## Not run: vars <- 10 rows <- 1000000 y <- sample(c("a", "b"), rows, TRUE) # Only categorical variables X1 <- as.data.frame(matrix(sample(letters[5:9], vars * rows, TRUE), ncol = vars)) nb_cat <- naive_bayes(x = X1, y = y) nb_cat system.time(pred2 <- predict(nb_cat, X1)) ## End(Not run)### Simulate example data n <- 100 set.seed(1) data <- data.frame(class = sample(c("classA", "classB"), n, TRUE), bern = sample(LETTERS[1:2], n, TRUE), cat = sample(letters[1:3], n, TRUE), logical = sample(c(TRUE,FALSE), n, TRUE), norm = rnorm(n), count = rpois(n, lambda = c(5,15))) train <- data[1:95, ] test <- data[96:100, -1] ### Fit the model with default settings nb <- naive_bayes(class ~ ., train) # Classification predict(nb, test, type = "class") nb %class% test # Posterior probabilities predict(nb, test, type = "prob") nb %prob% test ## Not run: vars <- 10 rows <- 1000000 y <- sample(c("a", "b"), rows, TRUE) # Only categorical variables X1 <- as.data.frame(matrix(sample(letters[5:9], vars * rows, TRUE), ncol = vars)) nb_cat <- naive_bayes(x = X1, y = y) nb_cat system.time(pred2 <- predict(nb_cat, X1)) ## End(Not run)
Classification based on the Non-Parametric Naive Bayes model.
## S3 method for class 'nonparametric_naive_bayes' predict(object, newdata = NULL, type = c("class","prob"), threshold = 0.001, eps = 0, ...)## S3 method for class 'nonparametric_naive_bayes' predict(object, newdata = NULL, type = c("class","prob"), threshold = 0.001, eps = 0, ...)
object |
object of class inheriting from |
newdata |
matrix with metric predictors (only numeric matrix accepted). |
type |
if "class", new data points are classified according to the highest posterior probabilities. If "prob", the posterior probabilities for each class are returned. |
threshold |
value by which zero probabilities or probabilities within the epsilon-range corresponding to metric variables are replaced (zero probabilities corresponding to categorical variables can be handled with Laplace (additive) smoothing). |
eps |
value that specifies an epsilon-range to replace zero or close to zero probabilities by |
... |
not used. |
This is a specialized version of the Naive Bayes classifier, in which all features take on real values (numeric/integer) and class conditional probabilities are non-parametrically estimated with kernel density estimator. By default Gaussian kernel is used and the smoothing bandwidth is selected according to the Silverman's 'rule of thumb'. For more details, please see the references and the documentation of density and bw.nrd0.
The Non-Parametric Naive Bayes is available in both, naive_bayes() and nonparametric_naive_bayes(). This specialized implementation of the Naive Bayes does not provide a substantial speed-up over the general naive_bayes() function but it should be more transparent and user friendly.
The nonparametric_naive_bayes function is equivalent to naive_bayes() when the numeric matrix or a data.frame contains only numeric variables and usekernel = TRUE.
The missing values (NAs) are omitted during the parameter estimation. The NAs in the newdata in predict.nonparametric_naive_bayes() are not included into the calculation of posterior probabilities; and if present an informative warning is given.
predict.nonparametric_naive_bayes returns either a factor with class labels corresponding to the maximal conditional posterior probabilities or a matrix with class label specific conditional posterior probabilities.
Michal Majka, [email protected]
Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall.
naive_bayes, nonparametric_naive_bayes, plot.nonparametric_naive_bayes, tables, get_cond_dist, naive_bayes, %class%
data(iris) y <- iris[[5]] M <- as.matrix(iris[-5]) ### Train the Non-Parametric Naive Bayes nnb <- nonparametric_naive_bayes(x = M, y = y, bw = "SJ") ### Classification head(predict(nnb, newdata = M, type = "class")) head(nnb %class% M) ### Posterior probabilities head(predict(nnb, newdata = M, type = "prob")) head(nnb %prob% M)data(iris) y <- iris[[5]] M <- as.matrix(iris[-5]) ### Train the Non-Parametric Naive Bayes nnb <- nonparametric_naive_bayes(x = M, y = y, bw = "SJ") ### Classification head(predict(nnb, newdata = M, type = "class")) head(nnb %class% M) ### Posterior probabilities head(predict(nnb, newdata = M, type = "prob")) head(nnb %prob% M)
Classification based on the Poisson Naive Bayes model.
## S3 method for class 'poisson_naive_bayes' predict(object, newdata = NULL, type = c("class","prob"), threshold = 0.001, eps = 0, ...)## S3 method for class 'poisson_naive_bayes' predict(object, newdata = NULL, type = c("class","prob"), threshold = 0.001, eps = 0, ...)
object |
object of class inheriting from |
newdata |
matrix with non-negative integer predictors (only numeric matrix is accepted). |
type |
if "class", new data points are classified according to the highest posterior probabilities. If "prob", the posterior probabilities for each class are returned. |
threshold |
value by which zero probabilities or probabilities within the epsilon-range corresponding to metric variables are replaced (zero probabilities corresponding to categorical variables can be handled with Laplace (additive) smoothing). |
eps |
value that specifies an epsilon-range to replace zero or close to zero probabilities by |
... |
not used. |
This is a specialized version of the Naive Bayes classifier, in which all features are non-negative integers and class conditional probabilities are modelled with the Poisson distribution.
Class posterior probabilities are calculated using the Bayes' rule under the assumption of independence of predictors. If no newdata is provided, the data from the object is used.
The Poisson Naive Bayes is available in both, naive_bayes and poisson_naive_bayes. The implementation of the specialized Naive Bayes provides more efficient performance though. The speedup comes from the restricting the data input to a numeric matrix and performing the linear algebra as well vectorized operations on it.
The NAs in the newdata are not included into the calculation of posterior probabilities; and if present an informative warning is given.
The poisson_naive_bayes function is equivalent to the naive_bayes function with usepoisson=TRUE and a numeric matrix or a data.frame containing only non-negative integer valued features (each variable has class "integer").
predict.poisson_naive_bayes returns either a factor with class labels corresponding to the maximal conditional posterior probabilities or a matrix with class label specific conditional posterior probabilities.
Michal Majka, [email protected]
poisson_naive_bayes, plot.poisson_naive_bayes, tables, get_cond_dist, %class%, coef.poisson_naive_bayes
cols <- 10 ; rows <- 100 M <- matrix(rpois(rows * cols, lambda = 3), nrow = rows, ncol = cols) # is.integer(M) # [1] TRUE y <- factor(sample(paste0("class", LETTERS[1:2]), rows, TRUE)) colnames(M) <- paste0("V", seq_len(ncol(M))) laplace <- 0 ### Train the Poisson Naive Bayes pnb <- poisson_naive_bayes(x = M, y = y, laplace = laplace) ### Classification head(predict(pnb, newdata = M, type = "class")) head(pnb %class% M) ### Posterior probabilities head(predict(pnb, newdata = M, type = "prob")) head(pnb %prob% M)cols <- 10 ; rows <- 100 M <- matrix(rpois(rows * cols, lambda = 3), nrow = rows, ncol = cols) # is.integer(M) # [1] TRUE y <- factor(sample(paste0("class", LETTERS[1:2]), rows, TRUE)) colnames(M) <- paste0("V", seq_len(ncol(M))) laplace <- 0 ### Train the Poisson Naive Bayes pnb <- poisson_naive_bayes(x = M, y = y, laplace = laplace) ### Classification head(predict(pnb, newdata = M, type = "class")) head(pnb %class% M) ### Posterior probabilities head(predict(pnb, newdata = M, type = "prob")) head(pnb %prob% M)
Auxiliary function for "naive_bayes" and "*_naive_bayes" objects for easy browsing tables.
tables(object, which = NULL)tables(object, which = NULL)
object |
object of class inheriting from: |
which |
tables to be showed (all by default). This can be any valid indexing vector or vector containing names of variables. |
Default print method for "naive_bayes" and "*_naive_bayes" objects shows at most five first tables. This auxiliary function tables returns by default all tables and allows easy subsetting via indexing variables.
list with tables.
Michal Majka, [email protected]
naive_bayes, bernoulli_naive_bayes, multinomial_naive_bayes, poisson_naive_bayes, gaussian_naive_bayes, tables, get_cond_dist
data(iris) nb <- naive_bayes(Species ~ ., data = iris) tables(nb, "Sepal.Length") tables(nb, c("Sepal.Length", "Sepal.Width")) tabs <- tables(nb, 1:2) tabs tabs[1]data(iris) nb <- naive_bayes(Species ~ ., data = iris) tables(nb, "Sepal.Length") tables(nb, c("Sepal.Length", "Sepal.Width")) tabs <- tables(nb, 1:2) tabs tabs[1]