Mean squared error

Kijk nog eens naar de NBA-voorspellingen van 2017. Elk jaar zijn er wel een paar NBA-teams die véél meer wedstrijden winnen dan verwacht. Als je de MAE gebruikt, straft deze nauwkeurigheidsmaat slechte voorspellingen minder hard dan wanneer je de MSE gebruikt. Door grote fouten te kwadrateren, lijken de resultaten minder goed.

In dit voorbeeld willen NBA-bestuurders het aantal overwinningen per team beter voorspellen. Je gebruikt de mean squared error om de voorspellingsfout te berekenen. De werkelijke overwinningen staan in y_test en de voorspellingen in predictions.

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Modelvalidatie in Python

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Bereken de MSE handmatig. $$ MSE = \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i ) ^2 }{n} $$
Bereken de MSE met sklearn.

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Probeer deze oefening eens door deze voorbeeldcode in te vullen.

from sklearn.metrics import ____

n = ___(predictions)
# Finish the manual calculation of the MSE
mse_one = sum((y_test - predictions)____) / n
print('With a manual calculation, the error is {}'.format(mse_one))

# Use the scikit-learn function to calculate MSE
mse_two = ____
print('Using scikit-learn, the error is {}'.format(mse_two))

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Deze oefening maakt deel uit van de cursus

Modelvalidatie 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: Train-, test- en validatiegegevenssets maken Exercise 2: Maak één holdoutset Exercise 3: Maak twee holdout-sets Exercise 4: Waarom holdout-sets gebruiken Exercise 5: Nauwkeurigheidsmaten: regressiemodellen Exercise 6: Gemiddelde absolute fout Exercise 7: Mean squared error

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Exercise 8: Prestaties op gegevenssubsets Exercise 9: Classificatiemetrics Exercise 10: Confusion matrices Exercise 11: Nogmaals confusion matrices Exercise 12: Precision vs. recall Exercise 13: De bias-variance trade-off Exercise 14: Fout door under/overfitting Exercise 15: Heb ik last van 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: The problems with holdout sets Exercise 2: Two samples Exercise 3: Potential problems Exercise 4: Cross-validation Exercise 5: scikit-learn's KFold()Exercise 6: Using KFold indices Exercise 7: sklearn's cross_val_score()Exercise 8: scikit-learn's methods Exercise 9: Implement cross_val_score()Exercise 10: Leave-one-out-cross-validation (LOOCV)Exercise 11: When to use 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!