Ein Modell mit Wertebereichen ausführen

Du hast gerade eine Liste von Hyperparametern und Wertebereichen erstellt, die du beim Tuning eines prädiktiven Modells für eine Aufgabe verwenden willst. Du hast max_depth, min_samples_split und max_features als Namen deiner Bereichsvariablen verwendet.

Diese Übung ist Teil des Kurses

<Kurs>Modellvalidierung in Python</Kurs>

Übungsanweisungen

Wähle zufällig einen max_depth, min_samples_split und max_features mithilfe deiner Bereichsvariablen aus.
Gib alle Parameter von rfr aus, um zu sehen, welche Werte zufällig ausgewählt wurden.

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

from sklearn.ensemble import RandomForestRegressor

# Fill in rfr using your variables
rfr = RandomForestRegressor(
    n_estimators=100,
    max_depth=random.____(____),
    min_samples_split=random.____(____),
    max_features=random.____(____))

# Print out the parameters
print(rfr.____)

Code bearbeiten und ausführen

Diese Übung ist Teil des Kurses

<Kurs>Modellvalidierung in Python</Kurs>

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4.9+

Kurs kostenlos starten

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: 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: Einführung in das Hyperparameter-Tuning Exercise 2: Hyperparameter erstellen Exercise 3: Ein Modell mit Wertebereichen ausführen

Aktuelle Übung

Exercise 4: RandomizedSearchCV Exercise 5: Vorbereitung für RandomizedSearch Exercise 6: RandomizedSearchCV implementieren Exercise 7: Dein finales Modell auswählen Exercise 8: Beste Klassifikationsgenauigkeit Exercise 9: Das beste Präzisionsmodell auswählen Exercise 10: Kurs abgeschlossen!