feature_importances_: ' ' [n_features] ' '形状的数组 注意:importance_type: string,default "gain", The feature importance type for the feature_importances_ property: either "gain", "weight", "cover", "total_gain" or "total_cover". 2、feature_importances_的原生代码 1. class XGBModel(X...
feature_importances_: ' ' [n_features] ' '形状的数组 注意:importance_type: string, default "gain", The feature importance type for the feature_importances_ property: either "gain", "weight", "cover", "total_gain" or "total_cover". 2、feature_importances_的原生代码 classXGBModel(XGBMod...
skplt.estimators.plot_learning_curve(rf, X, y) plt.show() scikitplot.estimators.plot_feature_importances可视化特征重要性。 import scikitplot as skplt rf = RandomForestClassifier() rf.fit(X, y) skplt.estimators.plot_feature_importances( rf, feature_names=['petal length', 'petal width', '...
skplt.estimators.plot_learning_curve(rf, X, y) plt.show() scikitplot.estimators.plot_feature_importances可视化特征重要性。 importscikitplotasskplt rf = RandomForestClassifier() rf.fit(X, y) skplt.estimators.plot_feature_importances( rf, feature_...
scikitplot.estimators.plot_feature_importances可视化特征重要性。 import scikitplot as skplt rf = RandomForestClassifier() rf.fit(X, y) skplt.estimators.plot_feature_importances( rf, feature_names=['petal length', 'petal width', 'sepal length', 'sepal width']) ...
skplt.estimators.plot_feature_importances( rf, feature_names=['petal length','petal width', 'sepal length','sepal width']) plt.show 功能3:聚类可视化 scikitplot.cluster.plot_elbow_curve展示聚类的肘步图。 importscikitplotasskplt kmeans = KMeans(random_state=1) ...
fromsklearn.model_selectionimporttrain_test_split fromsklearn.metricsimportaccuracy_score fromxgboostimportXGBClassifier fromxgboostimportplot_importance ### load datasets digits=datasets.load_digits() ### data analysis print(digits.data.shape)
from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from xgboost import XGBClassifier from xgboost import plot_importance ### load datasets digits = datasets.load_digits() ### data analysis print(digits.data.shape) ...
skplt.estimators.plot_feature_importances( rf, feature_names=['petal length','petal width', 'sepal length','sepal width']) plt.show() 功能3:聚类可视化 scikitplot.cluster.plot_elbow_curve展示聚类的肘步图。 importscikitplotasskplt kmeans = KMeans(random_state=1) ...
2、feature_importances_的原生代码 class XGBModel(XGBModelBase): # pylint: disable=too-many-arguments, too-many-instance-attributes, invalid-name """Implementation of the Scikit-Learn API for XGBoost. Parameters --- max_depth : int Maximum tree...