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)
Swipe to start coding
- Import the
RandomForestClassifierclass. - Create an instance of a
RandomForestClassifierclass with default parameters and train it. - Print the cross-validation score with the
cv=10of arandom_forestyou just built. - Print each feature's importance along with its name.
Solución
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Completion rate improved to 3.57Challenge: Implementing a Random Forest
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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)
Swipe to start coding
- Import the
RandomForestClassifierclass. - Create an instance of a
RandomForestClassifierclass with default parameters and train it. - Print the cross-validation score with the
cv=10of arandom_forestyou just built. - Print each feature's importance along with its name.
Solución
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