python-scikit-learnHow to get decision tree classifier feature importance
from sklearn import datasets, tree, model_selection, metrics
X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)
model = tree.DecisionTreeClassifier()
model.fit(X_train, y_train)
fi = model.feature_importances_ctrl + cfrom sklearn importimport module from scikit-learn | load_irisloads Iris dataset |
model_selection.train_test_splitsplits given | .DecisionTreeClassifier(creates decision tree classifier model |
.fit(train model with a given features and target variable dataset | .predict(predict target variable based on given features dataset |
.feature_importances_returns trained model feature importance list | |
Usage example
from sklearn import datasets, tree, model_selection, metrics
X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)
model = tree.DecisionTreeClassifier()
model.fit(X_train, y_train)
fi = model.feature_importances_
print(fi)output
[0. 0. 0.42490918 0.57509082]
Related
More of Python Scikit Learn
- How to get best parameters from grid search CV
- Get linear regression model coefficients
- Using OneClassSVM for outliers detection example
- Use imputer for one column only
- Feature selection using KBest and chi2
- How to inverse transform PCA reduced data
- Binary classification example
- Using Ridge regression model example
- Simple imputer example
- Using dbscan clustering example
See more codes...