sklearn.tree
.export_text¶
-
sklearn.tree.
export_text
(decision_tree, *, feature_names=None, max_depth=10, spacing=3, decimals=2, show_weights=False)[source]¶ Build a text report showing the rules of a decision tree.
Note that backwards compatibility may not be supported.
- Parameters
- decision_treeobject
The decision tree estimator to be exported. It can be an instance of DecisionTreeClassifier or DecisionTreeRegressor.
- feature_nameslist of str, default=None
A list of length n_features containing the feature names. If None generic names will be used (“feature_0”, “feature_1”, …).
- max_depthint, default=10
Only the first max_depth levels of the tree are exported. Truncated branches will be marked with “…”.
- spacingint, default=3
Number of spaces between edges. The higher it is, the wider the result.
- decimalsint, default=2
Number of decimal digits to display.
- show_weightsbool, default=False
If true the classification weights will be exported on each leaf. The classification weights are the number of samples each class.
- Returns
- reportstring
Text summary of all the rules in the decision tree.
Examples
>>> from sklearn.datasets import load_iris >>> from sklearn.tree import DecisionTreeClassifier >>> from sklearn.tree import export_text >>> iris = load_iris() >>> X = iris['data'] >>> y = iris['target'] >>> decision_tree = DecisionTreeClassifier(random_state=0, max_depth=2) >>> decision_tree = decision_tree.fit(X, y) >>> r = export_text(decision_tree, feature_names=iris['feature_names']) >>> print(r) |--- petal width (cm) <= 0.80 | |--- class: 0 |--- petal width (cm) > 0.80 | |--- petal width (cm) <= 1.75 | | |--- class: 1 | |--- petal width (cm) > 1.75 | | |--- class: 2