Classification Models API Reference
Functions
ChemometricsTools.GaussianDiscriminant
— Method.GaussianDiscriminant(M, X, Y; Factors = nothing)
Returns a GaussianDiscriminant classification model on basis object M
(PCA, LDA) and one hot encoded Y
.
ChemometricsTools.GaussianDiscriminant
— Method.( model::GaussianDiscriminant )( Z; Factors = size(model.ProjectedClassMeans)[2] )
Returns a 1 hot encoded inference from Z
using a GaussianDiscriminant object.
ChemometricsTools.GaussianNaiveBayes
— Method.GaussianNaiveBayes(X,Y)
Returns a GaussianNaiveBayes classification model object from X
and one hot encoded Y
.
ChemometricsTools.GaussianNaiveBayes
— Method.(gnb::GaussianNaiveBayes)(X)
Returns a 1 hot encoded inference from X
using a GaussianNaiveBayes object.
ChemometricsTools.KNN
— Type.KNN( X, Y; DistanceType::String )
DistanceType can be "euclidean", "manhattan". Y
Must be one hot encoded.
Returns a KNN classification model.
ChemometricsTools.KNN
— Method.( model::KNN )( Z; K = 1 )
Returns a 1 hot encoded inference from X
with K
Nearest Neighbors, using a KNN object.
ChemometricsTools.LogisticRegression
— Method.( model::LogisticRegression )( X )
Returns a 1 hot encoded inference from X
using a LogisticRegression object.
ProbabilisticNeuralNetwork( X, Y )
Stores data for a PNN. Y
Must be one hot encoded.
Returns a PNN classification model.
(PNN::ProbabilisticNeuralNetwork)(X; sigma = 0.1)
Returns a 1 hot encoded inference from X
with a probabilistic neural network.
MultinomialSoftmaxRegression(X, Y; LearnRate = 1e-3, maxiters = 1000, L2 = 0.0)
Returns a LogisticRegression classification model made by Stochastic Gradient Descent.