I believe that this answer is more correct than the other answers here: from sklearn.tree import _tree def tree_to_code(tree, feature_names): tree_ ... ... <看更多>
Search
Search
I believe that this answer is more correct than the other answers here: from sklearn.tree import _tree def tree_to_code(tree, feature_names): tree_ ... ... <看更多>
Besides, we split the data into two subsets to investigate how trees will predict values based on an out-of-samples dataset. from sklearn.model_selection import ... ... <看更多>
... <看更多>
沒有這個頁面的資訊。 ... <看更多>
In machine learning implementations of decision trees, the questions generally take the form of ... from sklearn.tree import DecisionTreeClassifier ... <看更多>
If we consult to its stable version's documentation, they seem to implement a version of CART, with categorial variables being unsupported. ... <看更多>
from sklearn.tree import DecisionTreeClassifier tree = DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0) tree.fit(X_train, y_train). ... <看更多>