未出现代码或错误:ValueError:max_features必须在(0,n_features]中。我已经尝试了堆栈解决方案,但没有得到解决方案。有人可以帮助吗?
def predict_RF(x_test_sel, k_vetor, y_train):
model = RandomForestRegressor()
model.fit(k_vetor, y_train)
y_predict = model.predict(x_test_sel)
kf = KFold(n_splits=3)
n_estimators = [25, 50, 75, 100]
max_features = [0.2, 0,7, 0.5, 1.0]
min_samples_leaf = [1, 2, 5, 10]
hyperF = dict (n_estimators = n_estimators, max_features=max_features, min_samples_leaf = min_samples_leaf)
gridF = GridSearchCV(model, hyperF, cv = kf, verbose = 1, n_jobs = -1)
grid_fit = gridF.fit(k_vetor, y_train) #Fit the gridsearch object with X_train, (k_vetor, y_train) -> dar nome x_train para k_vetor
print(grid_fit.best_params_)
return (y_predict)
2条答案
按热度按时间8zzbczxx1#
我在使用max_features作为float时遇到了同样的问题。我建议max_features列表应该只包含整数。例如:max_features = [2,5,7,10]
0x6upsns2#
请将0.7替换为0.7