NaiveBayesModel

Computes the conditional a-posterior probabilities of a categorical class
variable given independent predictor variables using Bayes rule.

Meta-package for statistical and machine learning with a unified
interface for model fitting, prediction, performance assessment, and
presentation of results.  Approaches for model fitting and prediction of
numerical, categorical, or censored time-to-event outcomes include
traditional regression models, regularization methods, tree-based methods,
support vector machines, neural networks, ensembles, data preprocessing,
filtering, and model tuning and selection.  Performance metrics are provided
for model assessment and can be estimated with independent test sets, split
sampling, cross-validation, or bootstrap resampling.  Resample estimation
can be executed in parallel for faster processing and nested in cases of
model tuning and selection.  Modeling results can be summarized with
descriptive statistics; calibration curves; variable importance; partial
dependence plots; confusion matrices; and ROC, lift, and other performance
curves.

Brian Smith

MachineShop

Machine Learning Models and Tools

NaiveBayesModel function

<dl><dt>laplace</dt>
<dd>positive numeric controlling Laplace smoothing.</dd></dl>

Arguments

Naive Bayes Classifier Model — NaiveBayesModel

<dl>

<dt>laplace</dt>
<dd>positive numeric controlling Laplace smoothing.</dd>

</dl>

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NaiveBayesModel: Naive Bayes Classifier Model

Description

Usage

Value

Arguments

Details

See Also

Examples