This time we did so by reducing complexity while maintaining performance.

Minimal cost complexity pruning recursively finds the node with the “weakest link”. The weakest link is characterized by an effective alpha, where the nodes with the smallest effective alpha are pruned first.

To get an idea of what values of ccp_alpha could be appropriate, scikit-learn provides shrubremover.bar_complexity_pruning_path that returns the effective alphas and the corresponding total leaf impurities at each step of the pruning.

Depending on the size, complexity, and quirks of the data, the optimal number of leaves could vary drastically.

Compute the pruning path during Minimal Cost-Complexity Pruning. decision_path (X[, check_input]) Return the decision path in the tree. fit (X, y[, sample_weight, check_input, ]) Build a decision tree classifier from the training set (X, y). get_depth Return the depth of the decision tree.

get_n_leaves.

Aug 16, You need to know that the TREE_LEAF constant is equal to def prune(decisiontree, min_samples_leaf = 1): if shrubremover.bar_samples_leaf >= min_samples_leaf: raise Exception('Tree already more pruned') else: shrubremover.bar_samples_leaf = min_samples_leaf tree = shrubremover.bar_ for i in range(shrubremover.bar_count): n_samples = tree.n_node_samples[i] if n_samples.

Compute the pruning path during Minimal Cost-Complexity Pruning. decision_path (X[, check_input]) Return the decision path in the tree.

It is also known as the Gini importance.

fit (X, y[, sample_weight, check_input, ]) Build a decision tree regressor from the training set (X, y). get_depth Return the depth of the decision tree. get_n_leaves. Jun 14, In scikit-learns DecisionTreeClassifier, ccp_alpha Is the cost-complexity parameter. Essentially, pruning recursively finds the node with the “weakest link.” The weakest link is characterized by an effective alpha, where the nodes with the smallest effective alpha are pruned shrubremover.bar: Edward Krueger.

Mar 16, @amueller / others: @jmschrei and i met to discuss the issue of post-pruning a few weeks ago, and we were unsure of how it would fit in the current scikit-learn API. Generally, post-pruning needs a validation set, but this doesn't seem to fit nicely with how the library is currently organized (namely, issues like creation / origin of the validation set and whether this would be an.