eval_set[i] = valid_x, self._le.transform(valid_y) super(LGBMClassifier, self).fit(X, _y, sample_weight=sample_weight, init_score=init_score, eval_set=eval_set, eval_names=eval_names, eval_sample_weight=eval_sample_weight, eval_class_weight=eval_class_weight, eval_init_score=eval_...
super(LGBMClassifier, self).fit(X, _y, sample_weight=sample_weight, init_score=init_score, eval_set=eval_set, eval_names=eval_names, eval_sample_weight=eval_sample_weight, eval_class_weight=eval_class_weight, eval_init_score=eval_init_score, eval_metric=eval_metric, early_stopping_rounds...
n_estimators=200, # 使用多少个弱分类器 objective='multiclass', num_class=3, booster='gbtree', min_child_weight=2, subsample=0.8, colsample_bytree=0.8, reg_alpha=0, reg_lambda=1, seed=0 # 随机数种子 ) model.fit(X_train,y_train, eval_set=[(X_train, y_train), (X_test, y_te...
class LGBMClassifier(LGBMModel, _LGBMClassifierBase): """LightGBM classifier.""" def fit(self, X, y, sample_weight=None, init_score=None, eval_set=None, eval_names=None, eval_sample_weight=None, eval_class_weight=None, eval_init_score=None, eval_metric=None, early_stopping_rounds=None,...
class LGBMClassifier(LGBMModel, _LGBMClassifierBase): """LightGBM classifier.""" def fit(self, X, y, sample_weight=None, init_score=None, eval_set=None, eval_names=None, eval_sample_weight=None, eval_class_weight=None, eval_init_score=None, eval_metric=None, ...
operators import make_pipeline t_no_op = TNoOp(name='no_action', datatypes='x', feat_constraints=[]) lgbm_classifier = LGBMClassifier(class_weight='balanced', learning_rate=0.18) pipeline = make_pipeline(t_no_op, lgbm_classifier) expected = \ """from lale.lib.autoai_libs import TNo...
min_child_weight=0.001 分支结点的最小权重 min_child_samples=20 random_state=None 随机种子数 n_jobs=-1 并行运行多线程核心数 silent=True 训练过程是否打印日志信息 verbose=-1 2、具体函数解释 class LGBMClassifier Found at: lightgbm.sklearn class LGBMClassifier(LGBMModel, _LGBMClassifierBase...
* Step 2: 改变 `max_depth` 和 `min_child_weight`.树模型复杂程度 这些参数对xgboost性能影响最大,因此,他们应该调整第一。 max_depth: 树的最大深度。增加这个值会使模型更加复杂,也容易出现过拟合,深度3-10是合理的。 min_child_weight: 正则化参数. 如果树分区中的实例权重小于定义的总和,则停止树构建...
num_class: default=1, type=int, alias=num_classes 只用于 multiclass 分类 reg_sqrt: default=false, type=bool 只用于 regression 适合sqrt(label) 相反, 预测结果也会自动转换成 pow2(prediction) 4、度量参数: metric:default={l2 for regression},{binary_logloss for binary classification},{ndcg for...
eval_class_weight=None, eval_init_score=None, eval_metric=None, early_stopping_rounds=None, verbose=True, feature_name='auto', categorical_feature='auto', callbacks=None): """Docstring is inherited from the LGBMModel.""" _LGBMAssertAllFinite(y) ...