Statistics and Machine Learning Toolbox

All Releases

R2015a (Version 10.0) - 5 Mar 2015

Version 10.0, part of Release 2015a, includes the following enhancements:

  • Classification app to train models and classify data using supervised machine learning
  • Statistical tests for comparing accuracies of two classification models using compareHoldout, testcholdout, and testckfold functions
  • Speedup of kmedoids, fitcknn, and other functions when using cosine, correlation, or spearman distance calculations
  • Performance enhancements for decision trees and performance curves​​
  • Additional option to control decision tree depth using 'MaxNumSplits' argument in fitctree, fitrtree, and templateTree functions
  • Code generation for pca and probability distribution functions (using MATLAB Coder)
  • Power and sample size for two-sample t-test using sampsizepwr function

See the Release Notes for details.

R2014b (Version 9.1) - 2 Oct 2014

Version 9.1, part of Release 2014b, includes the following enhancements:

  • Multiclass learning for support vector machines and other classifiers using the fitcecoc function
  • Generalized linear mixed-effects models using the fitglme function
  • Clustering that is robust to outliers using the kmedoids function
  • Speedup of the kmeans and gmdistribution clustering using the kmeans++ algorithm
  • Fisher's exact test for 2-by-2 contingency tables

See the Release Notes for details.

R2014a (Version 9.0) - 6 Mar 2014

Version 9.0, part of Release 2014a, includes the following enhancements:

  • Repeated measures modeling for data with multiple measurements per subject
  • fitcsvm function for enhanced performance of support vector machines (SVMs) for binary classification
  • evalclusters methods to expand the number of clusters and number of gap criterion simulations
  • p-value output from the multcompare function
  • mnrfit, lassoglm, and fitglm functions accept categorical variables as responses
  • Functions accept table inputs as an alternative to dataset array inputs
  • Functions and model properties return a table rather than a dataset array

See the Release Notes for details.

R2013b (Version 8.3) - 5 Sep 2013

Version 8.3, part of Release 2013b, includes the following enhancements:

  • Linear mixed-effects models
  • Code generation for probability distribution and descriptive statistics functions (using MATLAB Coder)
  • evaluatecluster function for estimating the optimal number of clusters in data
  • mvregress function that now accepts a design matrix even if Y has multiple columns
  • Upper tail probability calculations for cumulative distribution functions

See the Release Notes for details.

R2013a (Version 8.2) - 7 Mar 2013

Version 8.2, part of Release 2013a, includes the following enhancements:

  • Support vector machines (SVMs) for binary classification (formerly in Bioinformatics Toolbox)
  • Probabilistic PCA and alternating least-squares algorithms for principal component analysis with missing data
  • Anderson-Darling goodness-of-fit test
  • Decision-tree performance improvements and categorical predictors with many levels
  • Grouping and kernel density options in scatterhist function

See the Release Notes for details.