Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://scikit-learn.org/dev/developers/../api/../modules/generated/sklearn.tree.export_text.html below:

export_text — scikit-learn 1.8.dev0 documentation

sklearn.tree.export_text(decision_tree, *, feature_names=None, class_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_namesarray-like of shape (n_features,), default=None

An array containing the feature names. If None generic names will be used (“feature_0”, “feature_1”, …).

class_namesarray-like of shape (n_classes,), default=None

Names of each of the target classes in ascending numerical order. Only relevant for classification and not supported for multi-output.

• if None, the class names are delegated to decision_tree.classes_;

• otherwise, class_names will be used as class names instead of decision_tree.classes_. The length of class_names must match the length of decision_tree.classes_.

Added in version 1.3.

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:

reportstr

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

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4