Скачать с ютуб (HSMA 6 Day 14) 4J - Optimising ML: Imputation, Feature Engineering & Selection, Hyperparameters в хорошем качестве

(HSMA 6 Day 14) 4J - Optimising ML: Imputation, Feature Engineering & Selection, Hyperparameters

*Unfortunately the first 5 minutes or so of the lecture was not recorded* Covering a range of ways to improve your model's performance, including: Missing Data Imputation with SimpleImputer and IterativeImputer Feature Selection with SequentialFeatureSelector (forward and backward selection) and SelectFromModel (feature importance selection with model coefficients or mean decrease in impurity) Feature Engineering Dataset Splits (train/test/validation, k-fold) Dealing with Imbalanced Datasets with model parameters Hyperparameter tuning with exhaustive gridsearch, randomised gridsearch, and the Optuna framework Additional areas in the slides, but not covered in the video, are: ensemble models sklearn pipelines automatic model selection with the flaml library model calibration curves (reliability plots) Slides: https://docs.google.com/presentation/... Code Slides Only: https://docs.google.com/presentation/... Github Repository: https://github.com/hsma-programme/h6_... HSMA Website: https://sites.google.com/nihr.ac.uk/h...