Latest Features for Machine Learning

Latest Features

Learn about the latest MATLAB features for machine learning

Use the Classification Learner and Regression Learner apps to interactively explore data, select features, and train and evaluate supervised classification and regression models
New Perform automated tuning of hyperparameters and apply cost matrices from within the learner apps
Fit data to a wide range of probability distributions and explore the effects of changing parameter values using the Distribution Fitter app

New Optimize model type and hyperparameters simultaneously
Automatically tune hyperparameters using Bayesian optimization
Automatically select a subset of relevant features using techniques like neighborhood component analysis (NCA) and feature ranking
Parallelize the execution of automated optimization methods on multiple cores using Parallel Computing Toolbox, and scale to clouds and clusters using MATLAB Parallel Server

Leverage commonly used algorithms for classification and regression, such as linear and generalized linear models, support vector machines, decision trees, ensemble methods, and more
Use popular clustering algorithms including k-means, k-mediods, hierarchical clustering, Gaussian mixture, and Hidden Markov models
New Use density-based spatial clustering of applications with noise (DBSCAN) and spectral clustering of arbitrary shapes
Run statistical and machine learning computations faster than with open-source tools

Explore the structure of your data and relationships between features through scatter plots, box plots, dendrograms, and other standard statistical visualizations
Use advanced dimensionality reduction algorithms like Stochastic Neighbor Embedding (t-SNE)
Visualize high-density data with improved scatter plots in the Classification Learner app
New Create confusion matrices from tall arrays

Automatically generate C/C++ code for many popular classification, regression, and clustering algorithms
New Deploy to devices with limited memory and/or power using fixed-point arithmetic
New Update parameters of deployed models such as SVM, linear models, and decision trees, without regenerating C/C++ prediction code

Use tall arrays with many classification, regression, and clustering algorithms to train models on data sets that do not fit in memory
Fit multiclass classification models, perform hyperparameter optimization, and specify cost with tall arrays
Use fast approximate means, quantiles, and non-stratified partitions on out-of-memory data

30 days of exploration at your fingertips.