📝 Exercise M5.01

In the previous notebook, we showed how a tree with 1 level depth works. The aim of this exercise is to repeat part of the previous experiment for a tree with 2 levels depth to show how such parameter affects the feature space partitioning.

We first load the penguins dataset and split it into a training and a testing sets:

import pandas as pd

penguins = pd.read_csv("../datasets/penguins_classification.csv")
culmen_columns = ["Culmen Length (mm)", "Culmen Depth (mm)"]
target_column = "Species"

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.

from sklearn.model_selection import train_test_split

data, target = penguins[culmen_columns], penguins[target_column]
data_train, data_test, target_train, target_test = train_test_split(
    data, target, random_state=0
)

Create a decision tree classifier with a maximum depth of 2 levels and fit the training data.

# Write your code here.

Now plot the data and the decision boundary of the trained classifier to see the effect of increasing the depth of the tree.

Hint: Use the class DecisionBoundaryDisplay from the module sklearn.inspection as shown in previous course notebooks.

Warning

At this time, it is not possible to use response_method="predict_proba" for multiclass problems. This is a planned feature for a future version of scikit-learn. In the mean time, you can use response_method="predict" instead.

# Write your code here.

Did we make use of the feature “Culmen Length”? Plot the tree using the function sklearn.tree.plot_tree to find out!

# Write your code here.

Compute the accuracy of the decision tree on the testing data.

# Write your code here.