![]() ![]() After tuning the model, you can scale up to use tall arrays. In this example, you can use functionality like stepwise regression, which is suited for iterative, in-memory model development. ![]() For performance while tuning the model, you can consider working with a small extraction of in-memory data before scaling up to the entire tall array. To experiment further, repeat this workflow with smaller tall arrays. constant model: 2.77e+04, p-value = 0Įven with the model simplified, it can be useful to further adjust the relationships between the variables and include specific interactions. R-squared: 0.833, Adjusted R-Squared: 0.833į-statistic vs. Number of observations: 16667, Error degrees of freedom: 16663 Alternatively, double click the variable in the Workspace to explore the properties interactively.ĭepDelay ~ 1 + DepTime + ArrDelay + Distance The model variable contains information about the fitted model as properties, which you can access using dot notation. The display indicates fit information, as well as coefficients and associated coefficient statistics. R-squared: 0.834, Adjusted R-Squared: 0.833į-statistic vs. Number of observations: 16667, Error degrees of freedom: 16653 Use binScatterPlot to examine the relationship between the Hr and DepDelay variables.ĭepDelay ~ 1 + Year + Month + DayofMonth + DayOfWeek + DepTime + ArrDelay + Distance + Hr The visualizations all trigger execution, similar to calling the gather function. To run the example using the local MATLAB session when you have Parallel Computing Toolbox, change the global execution environment by using the mapreducer function. ![]() When you perform calculations on tall arrays, MATLAB® uses either a parallel pool (default if you have Parallel Computing Toolbox™) or the local MATLAB session. The fundamental difference is that tall arrays typically remain unevaluated until you request that the calculations be performed. ![]() Instead of writing specialized code that takes into account the huge size of the data, such as with MapReduce, you can use tall arrays to work with large data sets in a manner similar to in-memory MATLAB arrays. This type of data consists of a very large number of rows (observations) compared to a smaller number of columns (variables). Tall arrays and tables are designed for working with out-of-memory data. This example shows how to perform statistical analysis and machine learning on out-of-memory data with MATLAB® and Statistics and Machine Learning Toolbox™. ![]()
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