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Latest Features

Learn about the latest MATLAB features for machine learning

R2020b Highlights for Machine Learning

AutoML

Automatically select the best model and associated hyperparameters for regression

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Simulink

Simulate and generate code and speed up training of SVM models in Simulink

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Model Interpretability

Obtain locally interpretable model-agnostic explanations by finding important predictors (LIME)

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Explore all of the Latest Machine Learning Features

Interactive Apps

  • 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

Related Products: Statistics and Machine Learning Toolbox

Video: Classify Data Using the Classification Learner App

Automated Model Optimization

  • New: Automatically select the best model and associated hyperparameters for regression and classification (fitrauto and fitcauto)

  • 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

Related Products: MATLAB Parallel ServerParallel Computing ToolboxStatistics and Machine Learning Toolbox

Machine Learning and Statistical Algorithms

  • 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: Obtain locally interpretable model-agnostic explanations by finding important predictors (LIME)

  • New: Train linear regression and binary classification models incrementally

  • New: Extrapolate partial class labels to the entire data set using graphs and self-trained models (fitsemigraph, fitsemiself)

  • 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

Related Products: Statistics and Machine Learning Toolbox

Data Visualization

  • 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

  • Create confusion matrices from tall arrays

 

Related Products: Statistics and Machine Learning Toolbox

Deployment and Simulink Integration

  • Automatically generate C/C++ code for many popular classification, regression, and clustering algorithms

  • Deploy to devices with limited memory and/or power using fixed-point and single precision arithmetic

  • Update parameters of deployed models such as SVM, linear models, and decision trees, without regenerating C/C++ prediction code

  • New: Simulate and generate code for SVM models in Simulink

 

Related Products: MATLAB CoderMATLAB CompilerStatistics and Machine Learning Toolbox

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Big Data 

  • 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

 

Related Products: Parallel Computing ToolboxStatistics and Machine Learning Toolbox

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