KFold-indices gebruiken

Je hebt splits al gemaakt, met indices voor de candy-data-gegevensset om 5-voudige cross-validatie uit te voeren. Om een beter beeld te krijgen van hoe goed het random forest-model van een collega zal presteren op nieuwe data, wil je dit model draaien op de vijf verschillende training- en validatie-indices die je zojuist hebt gemaakt.

In deze oefening gebruik je deze indices om de nauwkeurigheid van dit model te controleren met de vijf verschillende splits. Er is een for-lus voorzien om je hierbij te helpen.

Deze oefening maakt deel uit van de cursus

Modelvalidatie in Python

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Oefeninstructies

Gebruik train_index en val_index om de juiste indices van X en y aan te roepen bij het maken van training- en validatiegegevens.
Fit rfc met de trainingsgegevensset
Gebruik rfc om voorspellingen te maken voor de validatiegegevensset en print de validatienauwkeurigheid

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

rfc = RandomForestRegressor(n_estimators=25, random_state=1111)

# Access the training and validation indices of splits
for train_index, val_index in splits:
    # Setup the training and validation data
    X_train, y_train = X[____], y[____]
    X_val, y_val = X[____], y[____]
    # Fit the random forest model
    rfc.____(____, ____)
    # Make predictions, and print the accuracy
    predictions = rfc.____(____)
    print("Split accuracy: " + str(mean_squared_error(y_val, predictions)))

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Modelvalidatie in Python

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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: 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: De problemen met holdout-sets Exercise 2: Twee steekproeven Exercise 3: Mogelijke problemen Exercise 4: Cross-validatie Exercise 5: scikit-learn's KFold()Exercise 6: KFold-indices gebruiken

Huidige oefening

Exercise 7: sklearns cross_val_score()Exercise 8: Methode’s van scikit-learn Exercise 9: Implementeer cross_val_score()Exercise 10: Leave-one-out-cross-validation (LOOCV)Exercise 11: Wanneer gebruik je 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!