Challenge: Implementing a Random Forest

In sklearn, the classification version of Random Forest is implemented using the RandomForestClassifier:

You will also calculate the cross-validation accuracy using the cross_val_score() function:

In the end, you'll print the importance of each feature. The feature_importances_ attribute returns an array of importance scores — these scores represent how much each feature contributed to reducing Gini impurity across all the decision nodes where that feature was used. In other words, the more a feature helps split the data in a useful way, the higher its importance.

However, the attribute only gives the scores without feature names. To display both, you can pair them using Python’s zip() function:

python

This prints each feature name along with its importance score, making it easier to understand which features the model relied on most.

Opgave

Swipe to start coding

You are given a Titanic dataset stored as a DataFrame in the df variable.

Initialize the Random Forest model, set random_state=42, train it, and store the fitted model in the random_forest variable.
Calculate the cross-validation scores for the trained model using 10 folds, and store the resulting scores in the cv_scores variable.

Løsning

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Sektion 4. Kapitel 3

Challenge: Implementing a Random Forest

In sklearn, the classification version of Random Forest is implemented using the RandomForestClassifier:

You will also calculate the cross-validation accuracy using the cross_val_score() function:

However, the attribute only gives the scores without feature names. To display both, you can pair them using Python’s zip() function:

python

This prints each feature name along with its importance score, making it easier to understand which features the model relied on most.

Opgave

Swipe to start coding

You are given a Titanic dataset stored as a DataFrame in the df variable.

Initialize the Random Forest model, set random_state=42, train it, and store the fitted model in the random_forest variable.
Calculate the cross-validation scores for the trained model using 10 folds, and store the resulting scores in the cv_scores variable.

Løsning

Skift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder

Var alt klart?

Tak for dine kommentarer!

Sektion 4. Kapitel 3

Skift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder