mean_squared_error(ytest,ypred)**0.5得到的是什么

`mean_squared_error(ytest, ypred)**0.5`是指使用均方误差（Mean Squared Error，MSE）来评估模型在测试数据集上的表现，并将其转换为根均方误差（Root Mean Squared Error，RMSE）。MSE是衡量模型预测值与真实值之间差异的一种度量方法，它计算预测值与真实值之间差异的平方和的平均值。RMSE是MSE的平方根，它通常用于表示模型预测值与真实值之间的平均误差大小。因此，`mean_squared_error(ytest, ypred)**0.5`的结果是模型在测试数据集上的平均预测误差。

相关问题

#建模分析 import pandas as pd import numpy as np import statsmodels.api as sm from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error from sklearn.ensemble import GradientBoostingRegressor # 去除异常值 diabetes = diabetes[(diabetes['bmi'] > 10) & (diabetes['HbA1c_level'] < 15)] # 划分训练集和测试集 train, test = train_test_split(diabetes, test_size=0.3, random_state=42) # 构建线性回归模型 xtrain, ytrain = train.drop('diabetes', axis=1), train['diabetes'] xtest, ytest = test.drop('diabetes', axis=1), test['diabetes'] Xtrain = sm.add_constant(xtrain) Xtest = sm.add_constant(xtest) print(diabetes.info()) reg = sm.OLS(ytrain, Xtrain).fit() print(reg.summary()) # 计算线性回归的预测误差 ypred = reg.predict(Xtest) mse = mean_squared_error(ytest, ypred) rmse = np.sqrt(mse) print('Linear Regression RMSE:', rmse) # 构建GBDT模型 gbdt = GradientBoostingRegressor(learning_rate=0.3).fit(xtrain, ytrain) print('GBDT R^2:', gbdt.score(xtrain, ytrain)) # 计算GBDT的预测误差 ypred = gbdt.predict(xtest) mse = mean_squared_error(ytest, ypred) rmse = np.sqrt(mse) print('GBDT RMSE:', rmse)

这段代码是一个用于糖尿病数据集的建模分析，主要使用了线性回归和GBDT（梯度提升决策树）两种模型进行预测。在代码中，首先通过去除异常值的方法对数据进行预处理，然后将数据集划分为训练集和测试集。接着，使用Statsmodels库中的OLS函数构建线性回归模型，并计算线性回归的预测误差。同时，使用sklearn库中的GradientBoostingRegressor函数构建GBDT模型，并计算GBDT的预测误差。最后，输出线性回归和GBDT模型的预测误差。

以下这段代码是关于CatBoost模型的超参数调整，但里面好像不是在五倍交叉验证下做的分析，请问应该怎么加上五倍交叉验证呢？import os import time import pandas as pd from catboost import CatBoostRegressor from hyperopt import fmin, hp, partial, Trials, tpe,rand from sklearn.metrics import r2_score, mean_squared_error from sklearn.model_selection import train_test_split from sklearn.model_selection import KFold, cross_val_score as CVS, train_test_split as TTS 自定义hyperopt的参数空间 space = {"iterations": hp.choice("iterations", range(1, 30)), "depth": hp.randint("depth", 16), "l2_leaf_reg": hp.randint("l2_leaf_reg", 222), "border_count": hp.randint("border_count", 222), 'learning_rate': hp.uniform('learning_rate', 0.001, 0.9), } data = pd.read_csv(r"E:\exercise\synthesis\synthesis_dummy_2.csv") #验证随机森林填补缺失值方法是否有效 X = data.iloc[:,1:] y = data.iloc[:,0] Xtrain,Xtest,Ytrain,Ytest = TTS(X_wrapper,y,test_size=0.2,random_state=100) def epoch_time(start_time, end_time): elapsed_secs = end_time - start_time elapsed_mins = elapsed_secs / 60 return elapsed_mins, elapsed_secs 自动化调参并训练 def cat_factory(argsDict): estimator = CatBoostRegressor(loss_function='RMSE', random_seed=22, learning_rate=argsDict['learning_rate'], iterations=argsDict['iterations'], l2_leaf_reg=argsDict['l2_leaf_reg'], border_count=argsDict['border_count'], depth=argsDict['depth'], verbose=0) estimator.fit(Xtrain, Ytrain) val_pred = estimator.predict(Xtest) mse = mean_squared_error(Ytest, val_pred) return mse

要在这段代码中添加五倍交叉验证，你可以按照以下步骤进行修改：

1. 导入所需的包：

from sklearn.model_selection import KFold, cross_val_score

2. 设置五倍交叉验证的参数：

n_folds = 5

3. 修改数据划分部分，将数据划分为训练集和测试集：

Xtrain, Xtest, Ytrain, Ytest = train_test_split(X_wrapper, y, test_size=0.2, random_state=100)

4. 修改自动化调参并训练的函数，使用五倍交叉验证进行模型评估：

def cat_factory(argsDict):
    estimator = CatBoostRegressor(loss_function='RMSE', random_seed=22,
                                  learning_rate=argsDict['learning_rate'],
                                  iterations=argsDict['iterations'],
                                  l2_leaf_reg=argsDict['l2_leaf_reg'],
                                  border_count=argsDict['border_count'],
                                  depth=argsDict['depth'], verbose=0)
    
    # 设置五倍交叉验证
    kf = KFold(n_splits=n_folds)
    mse_scores = []
    
    for train_index, val_index in kf.split(Xtrain):
        X_train, X_val = Xtrain.iloc[train_index], Xtrain.iloc[val_index]
        Y_train, Y_val = Ytrain.iloc[train_index], Ytrain.iloc[val_index]
        
        estimator.fit(X_train, Y_train)
        val_pred = estimator.predict(X_val)
        mse = mean_squared_error(Y_val, val_pred)
        mse_scores.append(mse)
    
    mse_mean = np.mean(mse_scores)
    return mse_mean

5. 修改自定义hyperopt的参数空间部分，将模型评估函数替换为修改后的函数：

space = {"iterations": hp.choice("iterations", range(1, 30)),
         "depth": hp.randint("depth", 16),
         "l2_leaf_reg": hp.randint("l2_leaf_reg", 222),
         "border_count": hp.randint("border_count", 222),
         'learning_rate': hp.uniform('learning_rate', 0.001, 0.9),
         }

trials = Trials()
best = fmin(fn=cat_factory, space=space, algo=tpe.suggest, max_evals=100, trials=trials)

这样，你就在代码中添加了五倍交叉验证来评估CatBoost模型的超参数调整效果。