lgbm调参指南-记录 基本调参思路,首先设置lr=0.1确定树的数量,然后调整每颗树的内部参数到最佳。确定树的内部参数后,用该参数,降低lr,反调lr和树的数量。 参数介绍: AI检测代码解析 boosting_type :‘gbdt’,‘rf’ n_jobs 几核cpu silent 默认选择True,选择False会输出很多建模中的细节,作用不大还刷屏。 --...
verbose_eval 每间隔n个迭代就显示一次进度 stratified 默认True,是否采用分层抽样,建议采用。 shuffle 默认True,是否洗牌,不建议采用。 seed 相当于random_state param需要填写的参数 objective 树的类型。回归:regression;二分类:binary;多分类:multiclass;排序等。 boosting 有gbdt,rf,dart。 n_jobs learning_rate n...
可以通过直接输入{class_label: weight}来对每个类别权重进行赋值,如{0:0.3,1:0.7}就是指类型0的权重为30%,而类型1的权重为70%。也可以通过选择‘balanced’来自动计算类型权重,实际计算公式为: n_samples / (n_classes * np.bincount(y)),当然这是不需要我们计算。在出现误分类代价很高或类别不平衡的情况...
n_jobs=-1, device_type='gpu', n_estimators=400, learning_rate=0.1, max_depth=5, num_leaves=31, colsample_bytree=0.51, subsample=0.6,#max_bins=127,) scores= cross_val_score(lgb_clf, X=train_x, y=train_y, verbose=1, cv = 5, scoring=make_scorer(accuracy_score), n_jobs=-1) ...
问在LGBM (Sklearn )和Optuna中使用早期停止的自定义eval度量EN在kaggle机器学习竞赛赛中有一个调参神器...
def create_lightgbm_classifier(X, y): lgbm = LGBMClassifier(boosting_type='gbdt', learning_rate=0.1, max_depth=5, n_estimators=200, n_jobs=1, random_state=777) model = lgbm.fit(X, y) return model Example #28Source File: test_misc_explainers.py From interpret-community with MIT Licen...
self._n_classes = len(self._classes) if self._n_classes > 2: Switch to using a multiclass objective in the underlying LGBM instance ova_aliases = "multiclassova", "multiclass_ova", "ova", "ovr" if self._objective not in ova_aliases and not callable(self. ...
lgb_ranker = lgb.LGBMRanker( boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1, max_depth=-1, n_estimators=300, objective='binary', subsample=0.7, colsample_bytree=0.7, subsample_freq=1, learning_rate=0.01, min_child_weight=50, random_state=2018, n_jobs=-1) g_tra...
if self._n_classes > 2: # Switch to using a multiclass objective in the underlying LGBM instance ova_aliases = "multiclassova", "multiclass_ova", "ova", "ovr" if self._objective not in ova_aliases and not callable(self. _objective): ...
if self._n_classes > 2: # Switch to using a multiclass objective in the underlying LGBM instance ova_aliases = "multiclassova", "multiclass_ova", "ova", "ovr" if self._objective not in ova_aliases and not callable(self. _objective): ...