max_features = non/auto becomes bagged trees
Random forests provide an improvement over bagged trees by way of a small tweak that decorrelates the trees. As in bagging, we build a number of decision trees on boostrapped training samples. But when building these decision trees, each time a split in a tree is considered, a random sample of m predictors is chosen as split candidates from the full set of p predictors.
Empirical good default values are
Number of tried attributes the default is square root of the whole number of attributes, yet usually the forest is not very sensitive about the value of this parameter -- in fact it is rarely optimized, especially because stochastic aspect of RF may introduce larger variations.
Random forests provide an improvement over bagged trees by way of a small tweak that decorrelates the trees. As in bagging, we build a number of decision trees on boostrapped training samples. But when building these decision trees, each time a split in a tree is considered, a random sample of m predictors is chosen as split candidates from the full set of p predictors.
Empirical good default values are
max_features=n_features for regression problems, and max_features=sqrt(n_features) for classification tasksNumber of tried attributes the default is square root of the whole number of attributes, yet usually the forest is not very sensitive about the value of this parameter -- in fact it is rarely optimized, especially because stochastic aspect of RF may introduce larger variations.
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