GaussianNBClassifier

GaussianNBClassifier

A model type for constructing a Gaussian naive Bayes classifier, based on NaiveBayes.jl, and implementing the MLJ model interface.

From MLJ, the type can be imported using

GaussianNBClassifier = @load GaussianNBClassifier pkg=NaiveBayes

Do model = GaussianNBClassifier() to construct an instance with default hyper-parameters.

Given each class taken on by the target variable y, it is supposed that the conditional probability distribution for the input variables X is a multivariate Gaussian. The mean and covariance of these Gaussian distributions are estimated using maximum likelihood, and a probability distribution for y given X is deduced by applying Bayes' rule. The required marginal for y is estimated using class frequency in the training data.

Important. The name "naive Bayes classifier" is perhaps misleading. Since we are learning the full multivariate Gaussian distributions for X given y, we are not applying the usual naive Bayes independence condition, which would amount to forcing the covariance matrix to be diagonal.

Training data

In MLJ or MLJBase, bind an instance model to data with

mach = machine(model, X, y)

Here:

X is any table of input features (eg, a DataFrame) whose columns are of scitype Continuous; check the column scitypes with schema(X)
y is the target, which can be any AbstractVector whose element scitype is Finite; check the scitype with schema(y)

Train the machine using fit!(mach, rows=...).

Operations

predict(mach, Xnew): return predictions of the target given new features Xnew, which should have the same scitype as X above. Predictions are probabilistic.
predict_mode(mach, Xnew): Return the mode of above predictions.

Fitted parameters

The fields of fitted_params(mach) are:

c_counts: A dictionary containing the observed count of each input class.
c_stats: A dictionary containing observed statistics on each input class. Each class is represented by a DataStats object, with the following fields:
- n_vars: The number of variables used to describe the class's behavior.
- n_obs: The number of times the class is observed.
- obs_axis: The axis along which the observations were computed.
gaussians: A per class dictionary of Gaussians, each representing the distribution of the class. Represented with type Distributions.MvNormal from the Distributions.jl package.
n_obs: The total number of observations in the training data.

Examples

using MLJ
GaussianNB = @load GaussianNBClassifier pkg=NaiveBayes

X, y = @load_iris
clf = GaussianNB()
mach = machine(clf, X, y) |> fit!

fitted_params(mach)

preds = predict(mach, X) ## probabilistic predictions
preds[1]
predict_mode(mach, X) ## point predictions