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Challenge: Implementing a Random Forest

In this chapter, you will build a Random Forest using the same titanic dataset.

Also, you will calculate the cross-validation accuracy using the cross_val_score() function

In the end, you will print the feature importances.
The feature_importances_ attribute only holds an array with importances without specifying the name of a feature.
To print the pairs ('name', importance), you can use the following syntax:

for f in zip(X.columns, model.feature_importances_):
    print(f)

Task

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Import the RandomForestClassifier class.
Create an instance of a RandomForestClassifier class with default parameters and train it.
Print the cross-validation score with the cv=10 of a random_forest you just built.
Print each feature's importance along with its name.

Solution

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Section 4. Chapter 3

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Challenge: Implementing a Random Forest

In this chapter, you will build a Random Forest using the same titanic dataset.

Also, you will calculate the cross-validation accuracy using the cross_val_score() function

for f in zip(X.columns, model.feature_importances_):
    print(f)

Task

Swipe to start coding

Import the RandomForestClassifier class.
Create an instance of a RandomForestClassifier class with default parameters and train it.
Print the cross-validation score with the cv=10 of a random_forest you just built.
Print each feature's importance along with its name.

Solution

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Swipe to show menu

Challenge: Implementing a Random Forest

Solution

Awesome!

Challenge: Implementing a Random Forest

Solution

Awesome!