early_stopping_rounds=None, verbose=True, feature_name='auto', categorical_feature='auto', callbacks=None): """Docstring is inherited from the LGBMModel.""" _LGBMAssertAllFinite(y) _LGBMCheckClassificationTargets(y) self._le = _LGBMLabelEncoder().fit(y) _y = self._le.transform(y) self...
early_stopping_rounds=None, verbose=True, feature_name='auto', categorical_feature='auto', callbacks=None): """Docstring is inherited from the LGBMModel.""" _LGBMAssertAllFinite(y) _LGBMCheckClassificationTargets(y) self._le = _LGBMLabelEncoder().fit(y) _y = self._le.transform(y) self...
early_stopping_rounds=None, verbose=True, feature_name='auto', categorical_feature='auto', callbacks=None): """Docstring is inherited from the LGBMModel.""" _LGBMAssertAllFinite(y) _LGBMCheckClassificationTargets(y) self._le = _LGBMLabelEncoder().fit(y) _y = self._le.transform(y) self...
early_stopping_rounds=None, verbose=True, feature_name='auto', categorical_feature='auto', callbacks=None): """Docstring is inherited from the LGBMModel.""" _LGBMAssertAllFinite(y) _LGBMCheckClassificationTargets(y) self._le = _LGBMLabelEncoder().fit(y) _y = self._le.transform(y) self...
用数字做索引, e.g. categorical_feature=0,1,2 意味着 column_0, column_1 和 column_2 是分类特征 为列名添加前缀 name:, e.g. categorical_feature=name:c1,c2,c3 意味着 c1, c2 和 c3 是分类特征 Note: 只支持分类与 int type. 索引从 0 开始. 同时它不包括标签栏 ...
titanic['embarked'] = pd.Categorical(titanic['embarked']).codes # Split the dataset into input features and the target variable X = titanic.drop('survived', axis=1) y = titanic['survived'] Training the LGBMClassifier Model To begin training the LGBMClassifier model, we need to split the ...
LGBMClassifier(boosting_type='gbdt', objective='binary', num_leaves=50, learning_rate=0.1, n_estimators=int(mo), max_depth=int(ze), bagging_fraction=0.9, feature_fraction=0.9, reg_lambda=0.2) model.fit(datadict[zhe]['train'][:, :-1], datadict[zhe]['train'][:, -1]) # 预测 ...
feature_name=feature_name, categorical_feature=categorical_feature, callbacks=callbacks) return self fit.__doc__ = LGBMModel.fit.__doc__ def predict(self, X, raw_score=False, num_iteration=None, pred_leaf=False, pred_contrib=False, **kwargs): """Docstring is inherited from the LGBMModel...
(dftrain.drop(['label'],axis = 1),label=dftrain['label'], categorical_feature = categorical_features,free_raw_data=False) lgb_valid = lgb.Dataset(dftest.drop(['label'],axis = 1),label=dftest['label'], categorical_feature = categorical_features, reference=lgb_train,free_raw_data=...
LGBMClassifier (1)初始化模型 from lightgbm import LGBMClassifier # 重要参数: lgb_model = LGBMClassifier( boosting_type='gbdt', num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=100, objective='binary', # 默认是二分类 min_split_gain=0.0, ...