Lære 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)

Oppgave

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.

Løsning

Bytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor

Alt var klart?

Takk for tilbakemeldingene dine!

Seksjon 4. Kapittel 3

single

Spør AI

Spør om hva du vil, eller prøv ett av de foreslåtte spørsmålene for å starte chatten vår

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)

Oppgave

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.

Løsning

Bytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor

Alt var klart?

Takk for tilbakemeldingene dine!

Seksjon 4. Kapittel 3

single

Sveip for å vise menyen

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)

Oppgave

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.

Løsning

Bytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor

Alt var klart?

Takk for tilbakemeldingene dine!

Challenge: Implementing a Random Forest

Løsning

Awesome!

Challenge: Implementing a Random Forest

Løsning

Awesome!

Challenge: Implementing a Random Forest

Løsning