Machine Learning

The Wolfram Language includes a wide range of integrated machine learning capabilities, from highly automated functions like Predict and Classify to functions based on specific methods and diagnostics. The functions work on many types of data, including numerical, categorical, time series, textual, and image.


General Supervised Learning

Classify classify data into categories using a built-in classifier or learning from examples

ClassifierFunction symbolic representation of a classifier to be applied to data

Predict predict values from data using a built-in predictor or learning from examples

PredictorFunction symbolic representation of a predictor to be applied to data

ClassifierMeasurements, PredictorMeasurements performance on test data

ClassifierInformation, PredictorInformation model information etc.

PerformanceGoal  ▪  Method  ▪  UtilityFunction  ▪  ClassPriors  ▪  IndeterminateThreshold

ImageIdentify, ImageInstanceQ automatically recognize objects in images

Specific Methods for Supervised Learning

Nearest, NearestNeighborGraph find nearest neighbors

FindFit find a generalized nonlinear fit

LinearModelFit  ▪  LogitModelFit  ▪  NonlinearModelFit  ▪  GeneralizedLinearModelFit  ▪  ProbitModelFit

TimeSeriesModelFit fit a wide variety of types of time series

Interpolation find an interpolation of values in a dataset

FindFormula find a simple symbolic formula for data

FindSequenceFunction find a function to reproduce a discrete sequence

FindHiddenMarkovStates find the most probable path in a Markov model

General Unsupervised Learning

DimensionReduce project data onto lower-dimensional space

DimensionReduction  ▪  DimensionReducerFunction

FeatureExtraction learn a feature extractor function from data

FeatureExtract  ▪  FeatureExtractorFunction

ClusterClassify classify data into clusters

FindDistribution find a simple symbolic distribution from data

Specific Methods Unsupervised Learning

Eigensystem  ▪  SingularValueDecomposition  ▪  PrincipalComponents  ▪  KarhunenLoeveDecomposition

FindClusters find clusters in numerical, textual, image, etc. data

ClusteringTree  ▪  ClusteringComponents

FindGraphCommunities find communities or clusters in graphs

SmoothKernelDistribution find kernel density estimates for data

FindHiddenMarkovStates infer hidden Markov states from a sequence of data

Reinforcement Learning & Optimization

BayesianMinimization model-based minimization of arbitrary objective functions

Neural Networks »

NetGraph represent an arbitrary neural network structure

NetChain  ▪  DotPlusLayer  ▪  ConvolutionLayer  ▪  PoolingLayer  ▪  ...

NetTrain train any neural network on CPUs, GPUs, etc.

Machine Learning Options

FeatureExtractor how to extract features to learn from

FeatureTypes feature types to assume for input data

PerformanceGoal whether to optimize for memory, quality, or speed

Preparing Data »

Standardize transform data to have zero mean and unit variance

Clip  ▪  Rescale  ▪  Threshold  ▪  LogisticSigmoid  ▪  ImageAdjust

CountsBy  ▪  GroupBy  ▪  SortBy  ▪  DeleteDuplicates

Filtering Data »

MovingAverage compute moving averages of lists, time series, etc.

GaussianFilter  ▪  MeanFilter  ▪  MeanShiftFilter  ▪  LowpassFilter  ▪  ...

Missing symbolic representation of missing elements in data