Nogmaals confusion matrices

Een confusion matrix maken in Python is eenvoudig. De grootste uitdaging is begrijpen hoe de matrix is georiënteerd. Deze oefening zorgt ervoor dat je de sklearn-implementatie van confusion matrices begrijpt. Hier heb je met de tic_tac_toe-gegevensset een random-forestmodel rfc gemaakt om uitkomsten van 0 (verlies) of 1 (winst) voor Speler Eén te voorspellen.

Noot: Als je over confusion matrices leest op een andere website of voor een andere programmeertaal, dan kunnen de waarden omgekeerd zijn.

Deze oefening maakt deel uit van de cursus

Modelvalidatie in Python

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Oefeninstructies

Importeer sklearn's functie om confusion matrices te maken.
Maak met het model rfc categorische voorspellingen op de testset X_test.
Maak een confusion matrix met sklearn.
Print de waarde uit cm die de werkelijke 1'en weergeeft die als 1 zijn voorspeld (true positives).

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

from sklearn.metrics import ____

# Create predictions
test_predictions = rfc.____(____)

# Create and print the confusion matrix
cm = ____(____, ____)
print(cm)

# Print the true positives (actual 1s that were predicted 1s)
print("The number of true positives is: {}".format(cm[____, ____]))

Code bewerken en uitvoeren

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 Exercise 8: Prestaties op gegevenssubsets Exercise 9: Classificatiemetrics Exercise 10: Confusion matrices Exercise 11: Nogmaals confusion matrices

Huidige oefening

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!