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. For a list of supported statistics functions, see Function List (Tall Arrays).

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.

Functions

binScatterPlot Scatter plot of bins for tall arrays

Topics

Logistic Regression with Tall Arrays
This example shows how to use logistic regression and other techniques to perform data analysis on tall arrays.

Featured Examples

Statistics and Machine Learning with Big Data Using Tall Arrays

Perform statistical analysis and machine learning on out-of-memory data with MATLAB® and Statistics and Machine Learning Toolbox™.

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Bayesian Optimization with Tall Arrays

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

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