Conteúdo do Curso
Classification with Python
5. Comparing Models
Classification with Python
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:
Tarefa
- 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.
Tudo estava claro?
Conteúdo do Curso
Classification with Python
5. Comparing Models
Classification with Python
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:
Tarefa
- 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.
Tudo estava claro?
