模型训练完成后,我们可以调用训练模型的plot_importance函数来获取特征的重要性。 plt.figure(figsize=(12,6)) lgb.plot_importance(model, max_num_features=30) plt.title("Featurertances") plt.show() 保存feature importance booster = model.booster_ importance = booster.feature_importance(importance_type='...
lgb.plot_importance(model,importance_type=“split”,figsize=(7,6),title=“LightGBM Feature Importance(Split)”)基于"split“度量创建特征重要性图。该指标衡量了在训练过程中使用某个特征来分割决策树中的数据的频率,这有助于评估该特征在决策中的重要性。 使用gain绘制特征重要性 # Plot feature importance u...
feature_importances_:一个数组,形状为[n_features]。如果base_estimator支持,则他给出每个特征的重要性。 oob_score_:一个浮点数,训练数据使用包外...。feature_importances_:一个数组,形状为[n_features]。如果base_estimator支持,则他给出每个特征的重要性。 oob_score_:一个浮点数,训练数据使用包外估计时...
lightgbm.plot_importance(): 绘制特征的重要性。 lightgbm.plot_importance(booster, ax=None, height=0.2, xlim=None, ylim=None, title='Feature importance', xlabel='Feature importance', ylabel='Features', importance_type='split', max_num_features=None, ignore_zero=True, figsize=None, grid=True,...
本教程将详细介绍如何在Python中使用LightGBM进行特征选择与重要性评估,并提供相应的代码示例。加载数据首先,我们需要加载数据集并准备数据用于模型训练。...= lgb_model.feature_importance(importance_type='gain') print("Feature Impor...
feature importance #ax = lgb.plot_tree(bst, tree_index=3, figsize=(40, 20), show_info=['split_gain'])ax=lgb.create_tree_digraph(bst)filename='project7-5.png'withopen(filename,'w')asf:f.write(ax._repr_svg_())ax
print( Feature importances: , list(gbm.feature_importances_)) # 网格搜索,参数优化 estimator = LGBMClassifier(num_leaves=31) param_grid = { learning_rate : [0.01,0.1,1], n_estimators : [20,40] } gbm = GridSearchCV(estimator, param_grid) ...
print('Feature importances:', list(gbm.feature_importances_)) # 网格搜索,参数优化 estimator = LGBMClassifier(num_leaves=31) param_grid = { 'learning_rate': [0.01, 0.1, 1], 'n_estimators': [20, 40] } gbm = GridSearchCV(estimator, param_grid) ...
# 特征重要度print('Feature importances:', list(gbm.feature_importances_)) # 网格搜索,参数优化estimator = LGBMClassifier(num_leaves=31)param_grid = {'learning_rate': [0.01, 0.1, 1],'n_estimators': [20, 40]}gbm = GridSearchCV(estimator, param_grid)gbm.fit(X_train, y_train)print('...
lightgbm 特征重要性选择 / 看所有特征哪个重要,print(pd.DataFrame({'column':feature_names,'importance':lgb_trained_model.feature_importance(),}).sort_values(by='importance'))