Heb ik last van underfitting?

Je maakt een random forest-model om te voorspellen of je een toekomstige pot boter-kaas-en-eieren wint. Met de tic_tac_toe-gegevensset heb je trainings- en testgegevenssets gemaakt: X_train, X_test, y_train en y_test.

Je hebt besloten om een reeks random forest-modellen te maken met verschillende aantallen bomen (1, 2, 3, 4, 5, 10, 20 en 50). Hoe meer bomen je gebruikt, hoe langer je random forest-model erover doet om te draaien. Maar als je niet genoeg bomen gebruikt, loop je het risico op underfitting. Je hebt een for-lus gemaakt om je model te testen bij de verschillende aantallen bomen.

Deze oefening maakt deel uit van de cursus

Modelvalidatie in Python

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Oefeninstructies

Voorspel in elke lus waarden voor zowel de gegevenssets X_train als X_test.
Voeg in elke lus de accuracy_score() van de y_train-gegevensset en de bijbehorende voorspellingen toe aan train_scores.
Voeg in elke lus de accuracy_score() van de y_test-gegevensset en de bijbehorende voorspellingen toe aan test_scores.
Print de training- en test-scores met de printstatements.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

from sklearn.metrics import accuracy_score

test_scores, train_scores = [], []
for i in [1, 2, 3, 4, 5, 10, 20, 50]:
    rfc = RandomForestClassifier(n_estimators=i, random_state=1111)
    rfc.fit(X_train, y_train)
    # Create predictions for the X_train and X_test datasets.
    train_predictions = rfc.predict(____)
    test_predictions = rfc.predict(____)
    # Append the accuracy score for the test and train predictions.
    train_scores.append(round(____(____, ____), 2))
    test_scores.append(round(____(____, ____), 2))
# Print the train and test scores.
print("The training scores were: {}".format(____))
print("The testing scores were: {}".format(____))

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Deze oefening maakt deel uit van de cursus

Modelvalidatie in Python

SkillTag.level.intermediateSkillTag.label

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Begin de cursus gratis

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 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?

Huidige oefening

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!