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
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).
|Scatter plot of bins for tall arrays|
List of Statistics and Machine Learning Toolbox functions that support 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™.
This example shows how to use logistic regression and other techniques to perform data analysis on tall arrays.
Perform Bayesian optimization to select optimal parameters for training a kernel classifier on tall arrays.