确认sklearn.metrics模块中是否存在root_mean_squared_error函数: 在sklearn.metrics模块中,root_mean_squared_error并不是一个标准的函数名。通常,我们计算均方根误差(Root Mean Squared Error, RMSE)使用的是mean_squared_error函数,然后取平方根。例如: python from sklearn.metrics import mean_squared_error impo...
importnumpyasnpfromsklearn.model_selectionimporttrain_test_splitfromsklearn.linear_modelimportLinearRegressionfromsklearn.metricsimportmean_squared_error# 准备数据X = np.array([1,2,3,4,5]).reshape(-1,1) y = np.array([2,4,6,8,10])# 划分训练集和测试集X_train, X_test, y_train, y_te...
Describe the bug For the sklearn.metrics.root_mean_squared_log_error(y_true, y_pred) & sklearn.metrics.mean_squared_log_error(y_true, y_pred) evaluation metrics, if any of the values in y_true or y_pred are below 0, the following ValueEr...
"from sklearn.metrics import root_mean_squared_error" ] }, { @@ -115,7 +115,7 @@ "\n", "y_pred = lr.predict(X_train)\n", "\n", "mean_squared_error(y_train, y_pred, squared=False)" "root_mean_squared_error(y_train, y_pred)" ] }, { @@ -288,7 +288,7 @@ "\...
To compute the Root Mean Squared Error (RMSE) in regression validation prediction, you can use the mean_squared_error function from the sklearn.metrics library in Python. Here's an example code snippet: Python Copy >>> from sklearn.metrics import mean_squared_error >>> y_true = [3,...
Root Mean Squared Error is the square root of Mean Squared Error (MSE). This is the same as Mean Squared Error (MSE) but the root of the value is considered while determining the accuracy of the model. import numpy as np import sklearn.metrics as metrics actual = np.array([56,45,68...
fromsklearn.model_selectionimportKFold fromsklearn.metricsimportmean_squared_error importcopy defPC_Cross_Validation(X,y,pc,cv): ''' x :光谱矩阵 nxm y :浓度阵 (化学值) pc:最大主成分数 cv:交叉验证数量 return : RMSECV:各主成分数对应的RMSECV ...
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使用到的模块: numpy pandas os matplotlib.pyplot seaborn warnings re sklearn.preprocessing.LabelEncoder sklearn.model_selection.train_test_split sklearn.metrics.r2_score sklearn.metrics.mean_squared_error sklearn.ensemble.RandomForestRegressor sklearn.ensemble.ExtraTreesRegressor sklearn.ensemble.GradientBoos...
使用到的模块: pandas os numpy matplotlib.pyplot seaborn missingno sklearn.model_selection.train_test_split keras.models.Sequential keras.layers.Dense keras.callbacks.EarlyStopping sklearn.metrics.mean_squared_error sklearn.ensemble.RandomForestRegressor xgboost.XGBRegressor lightgbm.LGBMRegressor scipy.stats...