# 定义交叉验证的函数deftime_series_cv(train_data,n_splits):fold_size=len(train_data)//n_splitsforiinrange(n_splits):train_fold=train_data[i*fold_size:(i+1)*fold_size]val_fold=train_data[(i+1)*fold_size:(i+2)*fold_size]yieldtrain_fold,val_fold n_splits=4# 定义折数fortrain...
K折交叉验证(k-fold cross validation) k-fold代码 分层交叉验证 (Stratified k-fold cross validation) 分层验证代码 重复交叉验证( k-fold cross validation with repetition) 重复验证代码 对抗验证(Adversarial Validation) 对抗验证代码 时间序列的交叉验证(Cross Validation for time series) 时间序列代码 交叉验证...
score= cross_val_score(logreg, X, Y, cv=stratifiedkf)print("Cross Validation Scores are {}".format(score))print("Average Cross Validation score :{}".format(score.mean())) 4、Leave P Out 交叉验证# Leave P Out 交叉验证是一种详尽的交叉验证技术,其中 p 样本用作验证集,剩余的 np 样本用...
pipeline = make_pipeline(StandardScaler(), LogisticRegression(max_iter=5000, class_weight='balanced')) # Perform 7-fold cross-validation and get predictions y_pred_L = cross_val_predict(pipeline, X, y_encoded, cv=7) # Calculate and print the confusion matrix cm_LR = confusion_matrix(y_en...
However, you must be careful while using this type of validation technique. Once the distribution of the test set changes, the validation set might no longer be a good subset to evaluate your model on. 6. Cross Validation for time series ...
cv: int, cross-validationgenerator or an iterable, default=None Determines the cross-validationsplitting strategy. Possible inputsforcv are: None, to use the default5-foldcross validation, int, to specify the number of foldsina (Stratified)KFold, ...
nf = NeuralForecast(models=models, freq='15min') nf_preds = nf.cross_validation(df=Y_df, val_size=val_size, test_size=test_size, n_windows=None) nf_preds = nf_preds.reset_index() 最后使用MAE和MSE来评估每个模型的性能。 ettm2_evaluation = evaluate(df=nf_preds, metrics=[mae, mse]...
nf=NeuralForecast(models=models,freq=freq)nf_preds=nf.cross_validation(df=Y_df,val_size=val_size,test_size=test_size,n_windows=None)nf_preds=nf_preds.reset_index() 评估计算了每个模型的平均绝对误差(MAE)和均方误差(MSE)。因为之前的数据是缩放的,因此报告的指标也是缩放的。
这就是为什么我们将使用技巧性更强的方法来优化模型参数的原因。我不知道这个方法是否有正式的名称,但是在CrossValidated上(在这个网站上你可以找到所有问题的答案,生命、宇宙以及任何事情的终极答案除外),有人提出了“滚动式交叉验证”(cross-validation on a rolling basis)这一名称。
nf_preds = nf.cross_validation(df=Y_df, val_size=val_size, test_size=test_size, n_windows=None) nf_preds = nf_preds.reset_index() 评估计算了每个模型的平均绝对误差(MAE)和均方误差(MSE)。因为之前的数据是缩放的,因此报告的指标也是缩放的。