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 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

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


Specific Methods Unsupervised Learning

Eigensystem  ▪  SingularValueDecomposition  ▪  PrincipalComponents  ▪  KarhunenLoeveDecomposition

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

ClusteringComponents find clusters based on value in arrays and images

FindGraphCommunities find communities or clusters in graphs

HiddenMarkovProcess find patterns in numerical data

SmoothKernelDistribution find kernel density estimates for data

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