{'colsample_bytree':0.5,'colsample_bylevel':0.5,'colsample_bynode':0.5} 在Python界面上,可以设置feature_weightsfor DMatrix来定义使用列采样时每个功能被选中的概率。fitsklearn界面中的方法有一个类似的参数 (9)lambda[默认= 1,别名:reg_lambda] L2正则化权重项(线性回归)。增加此值将使模型更加保守。 (...
feature_weights (默认: None)类型: array-like描述: 特征的权重。在树构建过程中,可以用来调节不同特征的重要性。非零值会改变分裂节点时的增益计算方式。 categorical_feature (默认: None)类型: list, int, dict描述: 指定哪些特征是类别特征(离散特征)。可以是特征索引的列表、掩码数组,或字典(键为特征索引,...
feature_weights(array_like)–每个要素的权重,定义使用colsample时每个要素被选中的概率。所有值必须大于0,否则将引发ValueError。 回调(回调函数列表)– 在每次迭代结束时应用的回调函数列表。通过使用Callback API可以使用预定义的回调。例: [xgb.callback.reset_learning_rate(custom_rates)] ...
例如,对于一个有64个feature的训练集,我们将这三个参数的值分别设置为{'colsample_bytree':0.5, 'colsample_bylevel':0.5, 'colsample_bynode':0.5},则每一次split模型能够用到的参数只有64*0.5*0.5*0.5=8个。 在Python界面中,当使用hist, gpu_hist或exact树方法时,可以为DMatrix设置feature_weights来定义使用...
feature_names: 一个字符串序列,给出了每一个特征的名字 feature_types: 一个字符串序列,给出了每个特征的数据类型 nthread:线程数 属性: feature_names: 返回每个特征的名字 feature_types: 返回每个特征的数据类型 方法: .get_base_margin(): 返回一个浮点数,表示DMatrix 的 base margin。
The feature importances (the higher, the more important the feature). oob_score_ : float Score of the training dataset obtained using an out-of-bag estimate. oob_decision_function_ : array of shape = [n_samples, n_classes] Decision function computed with out-of-bag estimate on the traini...
X : (2d-array like) Feature matrix with the first column the group label y : (optional, 1d-array like) target values sample_weights : (optional, 1d-array like) sample weights Returns --- sizes: (1d-array) group sizes X_features : (2d-array) features sorted per group y : ...
It controls the learning rate, i.e., the rate at which our model learns patterns in data. After every round, it shrinks the feature weights to reach the best optimum. Lower eta leads to slower computation. It must be supported by increase innrounds. ...
(self, X, y, sample_weight, base_margin, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set, base_margin_eval_set, feature_weights, callbacks) 786 obj = None 788 model, feval, params = self._configure_fit(xgb_model, eval_metric, params) --> ...
Balance the positive and negative weights,via scale_pos_weight UseAUCforevaluation If you care about predicting the right probability In such acase,you cannot re-balance the dataset In such acase,setparameter max_delta_step to a finitenumber(say1)will help convergence ...