Analyze and model data using statistics and machine learning

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Analyze and model data using statistics and machine learning

Statistics and Machine Learning Toolbox™ provides functions and apps to describe, analyze, and model data. You can use descriptive statistics and plots for exploratory data analysis, fit probability distributions to data, generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Regression and classification algorithms let you draw inferences from data and build predictive models.

For multidimensional data analysis, Statistics and Machine Learning Toolbox provides feature selection, stepwise regression, principal component analysis (PCA), regularization, and other dimensionality reduction methods that let you identify variables or features that impact your model.

The toolbox provides supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor, k-means, k-medoids, hierarchical clustering, Gaussian mixture models, and hidden Markov models. Many of the statistics and machine learning algorithms can be used for computations on data sets that are too big to be stored in memory.

Statistics and Machine Learning Toolbox Capabilities
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R2016b
 
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Capabilities

Exploratory Data Analysis

Explore data through statistical plotting with interactive graphics, algorithms for cluster analysis, and descriptive statistics for large data sets.

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Dimensionality Reduction

Model a continuous response variable as a function of one or more predictors.

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Machine Learning

Use algorithms that "learn" information directly from data without assuming a predetermined equation as a model.

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Regression and ANOVA

Use algorithms and functions to analyze multiple variables.

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Probability Distributions

Work with parametric and nonparametric probability distributions.

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Hypothesis Testing, DOE, and Statistical Process Control

Run statistical computations in parallel to gain speed and to reduce the execution time of your program or functions.

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Big Data, Parallel Computing, and Code Generation

Analyze whether sample-to-sample differences are significant and require further evaluation, or are consistent with data variation.

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Product Resources

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Documentation

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Functions

Browse the list of available Statistics and Machine Learning Toolbox functions.
System Requirements

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Technical Articles

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User Stories

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Community and Support

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Apps

Statistics and Machine Learning Toolbox apps enable you to quickly access common tasks through an interactive interface.
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R2016b
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Shyamal
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Statistics and Machine Learning Toolbox Technical Expert

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Statistics and Machine Learning Toolbox requires MATLAB.

Related Solutions

Use Statistics and Machine Learning Toolbox to solve scientific and engineering challenges:

Data Analytics
 Data Analytics
Test and Measurement
 Test and Measurement
Internet of Things
 Internet of Things
Computational Finance
 Computational Finance
Data Analysis
 Data Analysis
Image Processing and Computer Vision
 Image Processing and Computer Vision
Machine Learning
 Machine Learning
Mathematical Modeling
 Mathematical Modeling
Digital Signal Processing
 Digital Signal Processing
News and Events

Blog:  Loren on the Art of MATLAB
Blog: Loren on the Art of MATLAB
Turn ideas into MATLAB.
Machine Learning Made Easy

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