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Analysis of Big Data with Tall Arrays

Analyze out-of-memory data

Statistics and Machine Learning Toolbox™ contains a variety of functions that work with tall arrays. Tall arrays provide a convenient way to work with data that does not fit in memory, that is, the sample size can be arbitrarily large. To create a tall array, first create a datastore that references the data, and then use the tall function to convert the datastore into a tall array. For more information about tall arrays in MATLAB®, see Tall Arrays (MATLAB). For a list of supported statistics functions, see Tall Array Support, Usage Notes, and Limitations.

If you have Parallel Computing Toolbox™, then the use of parallel computing can speed up certain statistical computations with tall arrays. To use parallel computing with tall arrays, see Extend Tall Arrays with Other Products (MATLAB).


binScatterPlotScatter plot of bins for tall arrays


Tall Array Support, Usage Notes, and Limitations

List of Statistics and Machine Learning Toolbox functions that support tall arrays.

Statistics and Machine Learning with Big Data Using Tall Arrays

This example shows how to perform statistical analysis and machine learning on out-of-memory data with MATLAB® and Statistics and Machine Learning Toolbox™.

Logistic Regression with Tall Arrays

This example shows how to use logistic regression and other techniques to perform data analysis on tall arrays.

Bayesian Optimization with Tall Arrays

Perform Bayesian optimization to select optimal parameters for training a kernel classifier on tall arrays.