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.