Problemi potenziali

Quali delle seguenti affermazioni sono TRUE riguardo ai potenziali problemi con i campioni holdout:

A: Usare diversi metodi di suddivisione dei dati può portare ad avere dati diversi nei campioni holdout finali.
B: Se hai pochi dati, l’accuratezza sul holdout può essere fuorviante.
C: Non ci sono problemi. Creare un unico campione di train e test è l’unico modo per validare i modelli.
D: Non dovresti usare campioni holdout con pochi dati perché limiti i potenziali dati di training.

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Validazione dei modelli in Python

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Validazione dei modelli in Python

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Before we can validate models, we need an understanding of how to create and work with them. This chapter provides an introduction to running regression and classification models in scikit-learn. We will use this model building foundation throughout the remaining chapters.

Exercise 1: Introduction to model validation Exercise 2: Modeling steps Exercise 3: Seen vs. unseen data Exercise 4: Regression models Exercise 5: Set parameters and fit a model Exercise 6: Feature importances Exercise 7: Classification models Exercise 8: Classification predictions Exercise 9: Reusing model parameters Exercise 10: Random forest classifier

This chapter focuses on the basics of model validation. From splitting data into training, validation, and testing datasets, to creating an understanding of the bias-variance tradeoff, we build the foundation for the techniques of K-Fold and Leave-One-Out validation practiced in chapter three.

Exercise 1: Creating train, test, and validation datasets Exercise 2: Create one holdout set Exercise 3: Create two holdout sets Exercise 4: Why use holdout sets Exercise 5: Accuracy metrics: regression models Exercise 6: Mean absolute error Exercise 7: Mean squared error Exercise 8: Performance on data subsets Exercise 9: Classification metrics Exercise 10: Confusion matrices Exercise 11: Confusion matrices, again Exercise 12: Precision vs. recall Exercise 13: The bias-variance tradeoff Exercise 14: Error due to under/over-fitting Exercise 15: Am I underfitting?

Holdout sets are a great start to model validation. However, using a single train and test set if often not enough. Cross-validation is considered the gold standard when it comes to validating model performance and is almost always used when tuning model hyper-parameters. This chapter focuses on performing cross-validation to validate model performance.

Exercise 1: I limiti dei set di holdout Exercise 2: Due campioni Exercise 3: Problemi potenziali

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Exercise 4: Validazione incrociata Exercise 5: `KFold()` di scikit-learn Exercise 6: Utilizzare gli indici di KFold Exercise 7: cross_val_score() di sklearn Exercise 8: Metodi di scikit-learn Exercise 9: Usa cross_val_score()Exercise 10: Leave-one-out cross-validation (LOOCV)Exercise 11: Quando usare LOOCV Exercise 12: Leave-one-out cross-validation

The first three chapters focused on model validation techniques. In chapter 4 we apply these techniques, specifically cross-validation, while learning about hyperparameter tuning. After all, model validation makes tuning possible and helps us select the overall best model.

Exercise 1: Introduction to hyperparameter tuning Exercise 2: Creating Hyperparameters Exercise 3: Running a model using ranges Exercise 4: RandomizedSearchCV Exercise 5: Preparing for RandomizedSearch Exercise 6: Implementing RandomizedSearchCV Exercise 7: Selecting your final model Exercise 8: Best classification accuracy Exercise 9: Selecting the best precision model Exercise 10: Course completed!