![Kevin Markham on X: "π€β‘ scikit-learn tip #31: If you use cross-validation and your samples are NOT in an arbitrary order, shuffling may be required to get meaningful results. Use KFold or Kevin Markham on X: "π€β‘ scikit-learn tip #31: If you use cross-validation and your samples are NOT in an arbitrary order, shuffling may be required to get meaningful results. Use KFold or](https://pbs.twimg.com/media/EXWJH74UMAEUJgO.jpg)
Kevin Markham on X: "π€β‘ scikit-learn tip #31: If you use cross-validation and your samples are NOT in an arbitrary order, shuffling may be required to get meaningful results. Use KFold or
scoring of cross_val_score is not good especially with LeaveOneOut. Β· Issue #11434 Β· scikit-learn/scikit-learn Β· GitHub
![Complete tutorial on Cross Validation with Implementation in python using Sklearn. | by Deeksha Singh | Geek Culture | Medium Complete tutorial on Cross Validation with Implementation in python using Sklearn. | by Deeksha Singh | Geek Culture | Medium](https://miro.medium.com/v2/resize:fit:1400/1*6a30ftGDRRLGe22xDVHuMw.png)
Complete tutorial on Cross Validation with Implementation in python using Sklearn. | by Deeksha Singh | Geek Culture | Medium
![Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV in Scikit Learn - GeeksforGeeks Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV in Scikit Learn - GeeksforGeeks](https://media.geeksforgeeks.org/wp-content/uploads/20231220163947/download-(1).png)
Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV in Scikit Learn - GeeksforGeeks
![3.3. Metrics and scoring: quantifying the quality of predictions β scikit-learn 1.4.2 documentation 3.3. Metrics and scoring: quantifying the quality of predictions β scikit-learn 1.4.2 documentation](https://scikit-learn.org/stable/_images/sphx_glr_plot_cv_predict_001.png)
3.3. Metrics and scoring: quantifying the quality of predictions β scikit-learn 1.4.2 documentation
Prophet Analytica - Luckily, learning the sklearn library can help you run k-fold cross validation with one line of code: sklearn.model_selection. cross_val_score(estimator, X, y) Visit us at https://www.prophetanalytica.com to learn more! | Facebook
![cross validation - Why are sklearn's cross_val_score values not increasing with the size of the training set? - Cross Validated cross validation - Why are sklearn's cross_val_score values not increasing with the size of the training set? - Cross Validated](https://i.stack.imgur.com/Jh4as.png)