Stel parameters in en train een model

Voorspellende taken vallen in twee categorieën: regressie of classificatie. In de candy-gegevensset is de uitkomst een continue variabele die beschrijft hoe vaak de snoepsoort werd gekozen boven een andere in een reeks 1-op-1 matchups. Om deze waarde (het winstpercentage) te voorspellen, gebruik je een regressiemodel.

In deze oefening specificeer je een paar parameters met behulp van een random forest regressiemodel rfr.

Deze oefening maakt deel uit van de cursus

Modelvalidatie in Python

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Oefeninstructies

Voeg parameters toe aan rfr zodat het aantal te bouwen trees 100 is en de maximale diepte van deze trees 6.
Zorg dat het model reproduceerbaar is door een random state van 1111 toe te voegen.
Gebruik de .fit()-methode om het random forest regressiemodel te trainen met X_train als invoergegevens en y_train als response.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Set the number of trees
rfr.____ = ____

# Add a maximum depth
rfr.____ = ____

# Set the random state
rfr.____ = ____

# Fit the model
rfr.____(____, ____)

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: Introductie tot modelvalidatie Exercise 2: Modelleer-stappen Exercise 3: Geziene vs. ongeziene data Exercise 4: Regressiemodellen Exercise 5: Stel parameters in en train een model

Huidige oefening

Exercise 6: Belang van features Exercise 7: Classificatiemodellen Exercise 8: Classificatievoorspellingen Exercise 9: Modelparameters hergebruiken Exercise 10: Random forest-classificatie

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: 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!