classification or regression
criterion used for information gain calculation
maximum depth of the tree
maximum number of bins used for splitting features
algorithm for calculating quantiles
A map storing information about the categorical variables and the number of discrete values they take. For example, an entry (n -> k) implies the feature n is categorical with k categories 0, 1, 2, ... , k-1. It's important to note that features are zero-indexed.
maximum memory in MB allocated to histogram aggregation. Default value is 128 MB.
classification or regression
A map storing information about the categorical variables and the number of discrete values they take.
A map storing information about the categorical variables and the number of discrete values they take. For example, an entry (n -> k) implies the feature n is categorical with k categories 0, 1, 2, ... , k-1. It's important to note that features are zero-indexed.
criterion used for information gain calculation
maximum number of bins used for splitting features
maximum depth of the tree
maximum memory in MB allocated to histogram aggregation.
maximum memory in MB allocated to histogram aggregation. Default value is 128 MB.
algorithm for calculating quantiles
:: Experimental :: Stores all the configuration options for tree construction