π Solution for Exercise M6.02#
The aim of this exercise it to explore some attributes available in scikit-learnβs 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.
# solution
from sklearn.metrics import mean_absolute_error
from sklearn.ensemble import RandomForestRegressor
forest = RandomForestRegressor(n_estimators=3)
forest.fit(data_train, target_train)
target_predicted = forest.predict(data_test)
print(
"Mean absolute error: "
f"{mean_absolute_error(target_test, target_predicted):.3f} grams"
)
Mean absolute error: 350.641 grams
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.
# solution
import numpy as np
data_range = pd.DataFrame(np.linspace(170, 235, num=300), columns=data.columns)
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.
# solution
tree_predictions = []
for tree in forest.estimators_:
# we convert `data_range` into a NumPy array to avoid a warning raised in scikit-learn
tree_predictions.append(tree.predict(data_range.to_numpy()))
forest_predictions = forest.predict(data_range)
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.
# solution
import matplotlib.pyplot as plt
import seaborn as sns
sns.scatterplot(
data=penguins, x=feature_name, y=target_name, color="black", alpha=0.5
)
# plot tree predictions
for tree_idx, predictions in enumerate(tree_predictions):
plt.plot(
data_range[feature_name],
predictions,
label=f"Tree #{tree_idx}",
linestyle="--",
alpha=0.8,
)
plt.plot(data_range[feature_name], forest_predictions, label="Random forest")
_ = plt.legend(bbox_to_anchor=(1.05, 0.8), loc="upper left")
