📝 Exercise M5.01#

In the previous notebook, we showed how a tree with a depth of 1 level was working. The aim of this exercise is to repeat part of the previous experiment for a depth with 2 levels to show how the process of partitioning is repeated over time.

Before to start, we will:

  • load the dataset;

  • split the dataset into training and testing dataset;

  • define the function to show the classification decision function.

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. Once this classifier trained, plot the data and the decision boundary to see the benefit of increasing the depth. To plot the decision boundary, you should import the class DecisionBoundaryDisplay from the module sklearn.inspection as shown in the previous course notebook.

# 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.