✍🏻5 Scikit-learn tips from Data Scientist
1. Fill the gaps with Iterative Imputer - IterativeImputer, which iteratively searches for and fills in missing values, improving the dataset with each iteration. To use this function, import it enable_iterative_imputer from the sklearn.experimental package
2. Generate random dummy data to reserve the place where real or useful data should be present. The dummy data is needed for testing, so it must be reliable. To do this, you can use the functions make_classification () in a classification task or make_regression () in a regression task. You can also set the number of samples and features to control the behavior of the data in debugging and testing.
3. Save ML-models for reuse without retraining. To serialize your algorithms and save them, the pickle and joblib Python libraries come in handy.
4. Plot a confusion matrix using the plot_confusion_matrix function, which displays true positive, false positive, false negative and true negative values.
5. Visualize decision trees using the tree.plot_tree function in the matplotlib package without manually installing dependencies to create simple visualizations. You can also save the tree as a graphic png-file.
https://www.educative.io/blog/scikit-learn-tricks-tips