0

出现以下错误： ValueError: feature_names mismatch: ['f0', 'f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7', 'f8', 'f9' , 'f10'] ['Name', 'year', 'month', 'License', 'Facility Type', 'City_Chicago', 'Latitude', 'Longitude', 'Risk', 'Inspection Type', 'Violations '] 输入数据训练数据中预期的f10、f6、f5、f0、f3、f2、f4、f7、f9、f1、f8没有以下字段：名称、月份、经度、City_Chicago、纬度、许可证、风险、设施类型、年份、检验类型、违规

代码如下。

print(clf) 下面是输出

RandomizedSearchCV(cv=None, error_score=nan,
                   estimator=XGBClassifier(base_score=0.5, booster='gbtree',
                                           colsample_bylevel=1,
                                           colsample_bynode=1,
                                           colsample_bytree=1, gamma=0,
                                           learning_rate=0.2, max_delta_step=0,
                                           max_depth=7, min_child_weight=1,
                                           missing=None, n_estimators=1000,
                                           n_jobs=-1, nthread=None,
                                           objective='binary:logistic',
                                           random_state=2, reg_alpha=0,
                                           reg_lambda...
                                           verbosity=0),
                   iid='deprecated', n_iter=3, n_jobs=-1,
                   param_distributions={'xgbclassifier__learning_rate': [0.01,
                                                                         0.05,
                                                                         0.1,
                                                                         0.15,
                                                                         0.2],
                                        'xgbclassifier__max_delta_step': [1, 2,
                                                                          5],
                                        'xgbclassifier__max_depth': [3, 5, 7,
                                                                     9],
                                        'xgbclassifier__n_estimators': [100,
                                                                        200,
                                                                        500,
                                                                        1000]},
                   pre_dispatch='2*n_jobs', random_state=None, refit=True,
                   return_train_score=False, scoring='f1', verbose=1)

PDP 图

plt.rcParams['figure.dpi'] = 144

X_test_df = pd.DataFrame(X_test_processed, columns=X_test.columns)

feature = 'Violations'  # Permutation 최상위

pdp_isolated = pdp_isolate(
    model=clf, 
    dataset=X_test_df, 
    model_features=X_test_df.columns, 
    feature=feature
)

pdp_plot(pdp_isolated, feature_name=feature);

有什么问题？

4

0 回答 0