Het beste precisie-model selecteren

Je baas wil dit jaar drie sportwedstrijden voor je betalen. Van de 41 thuiswedstrijden die je favoriete team speelt, wil je zeker weten dat je naar drie thuiswedstrijden gaat die ze zéker winnen. Je bouwt een model om te bepalen welke wedstrijden je team gaat winnen.

Om dit te doen, bouw je een random search-algoritme en focus je op de precisie van het model (zodat je team inderdaad wint). Je wilt ook je beste model en beste hyperparameters bijhouden, zodat je ze volgend jaar opnieuw kunt gebruiken (als het model het goed doet, natuurlijk). Je hebt al gekozen voor het random forest-classificatiemodel rfc en een parameterverdeling param_dist aangemaakt.

Deze oefening maakt deel uit van de cursus

Modelvalidatie in Python

Cursus bekijken

Oefeninstructies

Maak een precision-scorer, precision, met make_scorer(<scoring_function>).
Maak de random search-methode af met rfc en param_dist.
Gebruik rs.cv_results_ om de gemiddelde testscores te printen.
Print de beste algehele score.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

from sklearn.metrics import precision_score, make_scorer

# Create a precision scorer
precision = ____(____)
# Finalize the random search
rs = RandomizedSearchCV(
  estimator=____, param_distributions=____,
  scoring = precision,
  cv=5, n_iter=10, random_state=1111)
rs.fit(X, y)

# print the mean test scores:
print('The accuracy for each run was: {}.'.format(rs.cv_results_['____']))
# print the best model score:
print('The best accuracy for a single model was: {}'.format(rs.____))

Code bewerken en uitvoeren

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: 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: Introductie tot hyperparameter tuning Exercise 2: Hyperparameters aanmaken Exercise 3: Een model draaien met bereiken Exercise 4: RandomizedSearchCV Exercise 5: Voorbereiden op RandomizedSearch Exercise 6: RandomizedSearchCV implementeren Exercise 7: Je definitieve model kiezen Exercise 8: Beste classificatienauwkeurigheid Exercise 9: Het beste precisie-model selecteren

Huidige oefening

Exercise 10: Cursus voltooid!