Useful for classification problems, if you have mapped a column's values to an array using mappings, you can choose to flatten it here so that each element becomes a new column.
Throws an error if any of the values in the given column is not an array, or their sizes differ from each other.
Names of the columns to be included in the labels and testLabels tensors.
Used for transforming values of entire columns. Key is column label, value is transformer function. Each value belonging to that column will be put through the transformer function and be overwritten with the return value of it.
If true, prepends a column of 1s to your features and testFeatures tensors.
If true, shuffles all rows with a fixed seed, meaning that shuffling the same data will always result in the same shuffled data.
You can pass a string instead of a boolean to customise the shuffle seed.
If true, splits your features and labels in half and moves them into testFeatures and testLabels.
If a number value is provided, splits that many rows and moved them into testFeatures and testLabels instead.
You can also pass it a percentage string, e.g. 10%. An error will be thrown if the string is not formatted correctly.
Calculates mean and variance for given columns using data only in features, then standardises the values in features and testFeatures. Does not touch labels.
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Names of the columns to be included in the features and testFeatures tensors.