Hands-On Machine Learning for Algorithmic Trading

How to regularize a decision tree

The following table lists key parameters available for this purpose in the sklearn decision tree implementation. After introducing the most important parameters, we will illustrate how to use cross-validation to optimize the hyperparameter settings with respect to the bias-variance tradeoff and lower prediction errors:

Parameter	Default	Options	Description
max_depth	None	int	Maximum number of levels: split nodes until reaching max_depth or all leaves are pure or contain fewer than min_samples_split samples.
max_features	None	None: all features; int float: fraction auto, sqrt: sqrt(n_features) log2: log2(n_features)	Number of features to consider for a split.
max_leaf_nodes ...