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:

python
9
1
2
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

import pandas as pd

from sklearn.ensemble import RandomForestClassifier

from sklearn.model_selection import cross_val_score

# Read the data and assign the variables

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/titanic.csv')

X = df.drop('Survived', axis=1)

y = df['Survived']

# Build and train a Random Forest

random_forest = RandomForestClassifier().fit(X, y)

# Print the cross-validation accuracy

print(cross_val_score(random_forest, X, y, cv=10).mean())

for feature in zip(X.columns, random_forest.feature_importances_):

print(feature)

Everything was clear?

Thanks for your feedback!

Section 4. Chapter 3

import pandas as pd

from sklearn.ensemble import ___

from sklearn.model_selection import cross_val_score

# Read the data and assign the variables

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/titanic.csv')

X = df.drop('Survived', axis=1)

y = df['Survived']

# Build and train a Random Forest

random_forest = ___().___(X, y)

# Print the cross-validation accuracy

print(cross_val_score(___, ___, ___, cv=10).mean())

for feature in zip(X.columns, random_forest.___):

print(feature)

Ask AI

Ask anything or try one of the suggested questions to begin our chat