相关疑难解决方法(0)

我需要传递一个参数,sample_weight我RandomForestClassifier喜欢这样:

X = np.array([[2.0, 2.0, 1.0, 0.0, 1.0, 3.0, 3.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0,
        1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 5.0, 3.0,
        2.0, '0'],
       [15.0, 2.0, 5.0, 5.0, 0.466666666667, 4.0, 3.0, 2.0, 0.0, 0.0, 0.0,
        0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0,
        7.0, 14.0, 2.0, '0'],
       [3.0, 4.0, 3.0, 1.0, 1.33333333333, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0,
        0.0, 1.0, 0.0, 0.0, 0.0, 0.0, …

early-stopping我想在 scikit-learns -方法中使用 - 选项GridSearchCV。SO- thread中显示了一个这样的示例：

import xgboost as xgb
from sklearn.model_selection import GridSearchCV

trainX= [[1], [2], [3], [4], [5]]
trainY = [1, 2, 3, 4, 5]

testX = trainX 
testY = trainY

param_grid = {"subsample" : [0.5, 0.8],
              "n_estimators" : [600]}

fit_params = {"early_stopping_rounds":1,
             "eval_set" : [[testX, testY]]}


model = xgb.XGBRegressor()
gridsearch = GridSearchCV(estimator  = xgb.XGBRegressor(), 
                          param_grid=param_grid,
                          fit_params=fit_params,                          
                          verbose=1,                          
                          cv=2)
gridsearch.fit(trainX,trainY)

但是，我想使用交叉验证过程的保留集作为验证集。有没有办法在 中指定这一点GridSearchCV？