Question

我正在尝试为随机森林回归算法构建 GridSearchCV，但面临“不支持连续”错误。这是我的代码如下：

import numpy as np
import math
from numpy import mean
from numpy import std
from numpy import absolute
from sklearn.linear_model import Ridge
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import KFold
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import RidgeCV
from sklearn.model_selection import RepeatedKFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer

X = data.drop(['HL', 'CL'], axis = 1).astype('float64')
y1 = data.drop(['RC', 'SA','WA','RA','OH','Orient.','GA','GAD','CL'], axis = 1).astype('float64')

X_train,X_test,y_train,y_test = train_test_split(X,y1,test_size=0.25,random_state=0)

RFReg = RandomForestRegressor()


parameters = {
    'n_estimators':(10,50,100,250,500),
    'max_depth': (50,150,250),
    'min_samples_split':(2,3),
    'min_samples_leaf':(1,2,3)
}


grid_search = GridSearchCV(estimator = RFReg,param_grid = parameters,n_jobs=-1,verbose=2,scoring='accuracy',cv=3)


grid_search.fit(X_train, y_train)

我正在接受最后一行的错误。

用于随机森林回归器参数调整的 GridSearchCV

0 个答案: