# Importance of decision tree hyperparameters on generalization#

In this notebook, we will illustrate the importance of some key hyperparameters on the decision tree; we will demonstrate their effects on the classification and regression problems we saw previously.

First, we will load the classification and regression datasets.

import pandas as pd

data_clf_columns = ["Culmen Length (mm)", "Culmen Depth (mm)"]
target_clf_column = "Species"

data_reg_columns = ["Flipper Length (mm)"]
target_reg_column = "Body Mass (g)"


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 helper functions#

We will create some helper functions to plot the data samples as well as the decision boundary for classification and the regression line for regression.

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.inspection import DecisionBoundaryDisplay

def fit_and_plot_classification(model, data, feature_names, target_names):
model.fit(data[feature_names], data[target_names])
if data[target_names].nunique() == 2:
palette = ["tab:red", "tab:blue"]
else:
palette = ["tab:red", "tab:blue", "black"]
DecisionBoundaryDisplay.from_estimator(
model, data[feature_names], response_method="predict",
cmap="RdBu", alpha=0.5
)
sns.scatterplot(data=data, x=feature_names[0], y=feature_names[1],
hue=target_names, palette=palette)
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')

def fit_and_plot_regression(model, data, feature_names, target_names):
model.fit(data[feature_names], data[target_names])
data_test = pd.DataFrame(
np.arange(data.iloc[:, 0].min(), data.iloc[:, 0].max()),
columns=data[feature_names].columns,
)
target_predicted = model.predict(data_test)

sns.scatterplot(
x=data.iloc[:, 0], y=data[target_names], color="black", alpha=0.5)
plt.plot(data_test.iloc[:, 0], target_predicted, linewidth=4)


## Effect of the max_depth parameter#

The hyperparameter max_depth controls the overall complexity of a decision tree. This hyperparameter allows to get a trade-off between an under-fitted and over-fitted decision tree. Let’s build a shallow tree and then a deeper tree, for both classification and regression, to understand the impact of the parameter.

We can first set the max_depth parameter value to a very low value.

from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor

max_depth = 2
tree_clf = DecisionTreeClassifier(max_depth=max_depth)
tree_reg = DecisionTreeRegressor(max_depth=max_depth)

fit_and_plot_classification(
tree_clf, data_clf, data_clf_columns, target_clf_column)
_ = plt.title(f"Shallow classification tree with max-depth of {max_depth}")

fit_and_plot_regression(
tree_reg, data_reg, data_reg_columns, target_reg_column)
_ = plt.title(f"Shallow regression tree with max-depth of {max_depth}")


Now, let’s increase the max_depth parameter value to check the difference by observing the decision function.

max_depth = 30
tree_clf = DecisionTreeClassifier(max_depth=max_depth)
tree_reg = DecisionTreeRegressor(max_depth=max_depth)

fit_and_plot_classification(
tree_clf, data_clf, data_clf_columns, target_clf_column)
_ = plt.title(f"Deep classification tree with max-depth of {max_depth}")

fit_and_plot_regression(
tree_reg, data_reg, data_reg_columns, target_reg_column)
_ = plt.title(f"Deep regression tree with max-depth of {max_depth}")


For both classification and regression setting, we observe that increasing the depth will make the tree model more expressive. However, a tree that is too deep will overfit the training data, creating partitions which are only correct for “outliers” (noisy samples). The max_depth is one of the hyperparameters that one should optimize via cross-validation and grid-search.

from sklearn.model_selection import GridSearchCV

param_grid = {"max_depth": np.arange(2, 10, 1)}
tree_clf = GridSearchCV(DecisionTreeClassifier(), param_grid=param_grid)
tree_reg = GridSearchCV(DecisionTreeRegressor(), param_grid=param_grid)

fit_and_plot_classification(
tree_clf, data_clf, data_clf_columns, target_clf_column)
_ = plt.title(f"Optimal depth found via CV: "
f"{tree_clf.best_params_['max_depth']}")

fit_and_plot_regression(
tree_reg, data_reg, data_reg_columns, target_reg_column)
_ = plt.title(f"Optimal depth found via CV: "
f"{tree_reg.best_params_['max_depth']}")


With this example, we see that there is not a single value that is optimal for any dataset. Thus, this parameter is required to be optimized for each application.

## Other hyperparameters in decision trees#

The max_depth hyperparameter controls the overall complexity of the tree. This parameter is adequate under the assumption that a tree is built symmetrically. However, there is no guarantee that a tree will be symmetrical. Indeed, optimal generalization performance could be reached by growing some of the branches deeper than some others.

We will build a dataset where we will illustrate this asymmetry. We will generate a dataset composed of 2 subsets: one subset where a clear separation should be found by the tree and another subset where samples from both classes will be mixed. It implies that a decision tree will need more splits to classify properly samples from the second subset than from the first subset.

from sklearn.datasets import make_blobs

data_clf_columns = ["Feature #0", "Feature #1"]
target_clf_column = "Class"

# Blobs that will be interlaced
X_1, y_1 = make_blobs(
n_samples=300, centers=[[0, 0], [-1, -1]], random_state=0)
# Blobs that will be easily separated
X_2, y_2 = make_blobs(
n_samples=300, centers=[[3, 6], [7, 0]], random_state=0)

X = np.concatenate([X_1, X_2], axis=0)
y = np.concatenate([y_1, y_2])
data_clf = np.concatenate([X, y[:, np.newaxis]], axis=1)
data_clf = pd.DataFrame(
data_clf, columns=data_clf_columns + [target_clf_column])
data_clf[target_clf_column] = data_clf[target_clf_column].astype(np.int32)

sns.scatterplot(data=data_clf, x=data_clf_columns[0], y=data_clf_columns[1],
hue=target_clf_column, palette=["tab:red", "tab:blue"])
_ = plt.title("Synthetic dataset")


We will first train a shallow decision tree with max_depth=2. We would expect this depth to be enough to separate the blobs that are easy to separate.

max_depth = 2
tree_clf = DecisionTreeClassifier(max_depth=max_depth)
fit_and_plot_classification(
tree_clf, data_clf, data_clf_columns, target_clf_column)
_ = plt.title(f"Decision tree with max-depth of {max_depth}")


As expected, we see that the blue blob on the right and the red blob on the top are easily separated. However, more splits will be required to better split the blob were both blue and red data points are mixed.

Indeed, we see that red blob on the top and the blue blob on the right of the plot are perfectly separated. However, the tree is still making mistakes in the area where the blobs are mixed together. Let’s check the tree representation.

from sklearn.tree import plot_tree

_, ax = plt.subplots(figsize=(10, 10))
_ = plot_tree(tree_clf, ax=ax, feature_names=data_clf_columns)


We see that the right branch achieves perfect classification. Now, we increase the depth to check how the tree will grow.

max_depth = 6
tree_clf = DecisionTreeClassifier(max_depth=max_depth)
fit_and_plot_classification(
tree_clf, data_clf, data_clf_columns, target_clf_column)
_ = plt.title(f"Decision tree with max-depth of {max_depth}")

_, ax = plt.subplots(figsize=(11, 7))
_ = plot_tree(tree_clf, ax=ax, feature_names=data_clf_columns)


As expected, the left branch of the tree continue to grow while no further splits were done on the right branch. Fixing the max_depth parameter would cut the tree horizontally at a specific level, whether or not it would be more beneficial that a branch continue growing.

The hyperparameters min_samples_leaf, min_samples_split, max_leaf_nodes, or min_impurity_decrease allows growing asymmetric trees and apply a constraint at the leaves or nodes level. We will check the effect of min_samples_leaf.

min_samples_leaf = 60
tree_clf = DecisionTreeClassifier(min_samples_leaf=min_samples_leaf)
fit_and_plot_classification(
tree_clf, data_clf, data_clf_columns, target_clf_column)
_ = plt.title(
f"Decision tree with leaf having at least {min_samples_leaf} samples")

_, ax = plt.subplots(figsize=(10, 7))
_ = plot_tree(tree_clf, ax=ax, feature_names=data_clf_columns)


This hyperparameter allows to have leaves with a minimum number of samples and no further splits will be searched otherwise. Therefore, these hyperparameters could be an alternative to fix the max_depth hyperparameter.