📝 Exercise M6.02

📝 Exercise M6.02#

The aim of this exercise it to explore some attributes available in scikit-learn’s random forest.

First, we will fit the penguins regression dataset.

import pandas as pd
from sklearn.model_selection import train_test_split

penguins = pd.read_csv("../datasets/penguins_regression.csv")
feature_name = "Flipper Length (mm)"
target_name = "Body Mass (g)"
data, target = penguins[[feature_name]], penguins[target_name]
data_train, data_test, target_train, target_test = train_test_split(
    data, target, random_state=0
)

Note

If you want a deeper overview regarding this dataset, you can refer to the Appendix - Datasets description section at the end of this MOOC.

Create a random forest containing three trees. Train the forest and check the generalization performance on the testing set in terms of mean absolute error.

# Write your code here.

We now aim to plot the predictions from the individual trees in the forest. For that purpose you have to create first a new dataset containing evenly spaced values for the flipper length over the interval between 170 mm and 230 mm.

# Write your code here.

The trees contained in the forest that you created can be accessed with the attribute estimators_. Use them to predict the body mass corresponding to the values in this newly created dataset. Similarly find the predictions of the random forest in this dataset.

# Write your code here.

Now make a plot that displays:

  • the whole data using a scatter plot;

  • the decision of each individual tree;

  • the decision of the random forest.

# Write your code here.