Visualizing Random Forest Decision Boundaries on the Wine Dataset with dtreeviz

Various classification models such as logistic regression, K‑nearest neighbors, decision trees, etc., can predict the class of unknown data based on features. This article uses the wine dataset to demonstrate how to evaluate predictions with accuracy, confusion matrix, and how to visualize decision boundaries and misclassifications using Random Forest and the dtreeviz library.

Data

We load the wine dataset with load_wine() . The dataset contains 13 numeric features for 178 samples. For this example we focus on the flavanoids and proline features, each sample belonging to one of three classes (0, 1, 2).

Model

We train a Random Forest classifier.

<code>import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_wine
from sklearn.metrics import confusion_matrix, accuracy_score
import matplotlib.pyplot as plt
import dtreeviz
from dtreeviz import decision_boundaries
wine = load_wine()
X = wine.data[:, [12, 6]]  # proline, flavanoids
y = wine.target
rf = RandomForestClassifier(n_estimators=50, min_samples_leaf=20, n_jobs=-1)
rf.fit(X, y)
</code>

Evaluation Results

Accuracy

Using accuracy_score we obtain an accuracy of 0.90.

<code>y_pred = rf.predict(X)
accuracy_score(y, y_pred)
</code>

Confusion Matrix

The confusion matrix visualizes correct and incorrect predictions.

<code>import seaborn as sn
sn.heatmap(confusion_matrix(y, y_pred), annot=True)
</code>

Original vs Predicted Data

Scatter plots of the two features colored by true class and by predicted class.

<code>fig,axes = plt.subplots(1,2,figsize=(8,3.8),dpi=300)
features = ['proline','flavanoids']
df1 = pd.DataFrame(X, columns=features)
df1['target'] = wine.target
df1['prediction'] = rf.predict(X)
sn.scatterplot(x='proline', y='flavanoids', hue='target', data=df1, ax=axes[0])
sn.scatterplot(x='proline', y='flavanoids', hue='prediction', data=df1, ax=axes[1])
</code>

Decision Boundaries

Using dtreeviz.decision_boundaries we plot the classification regions and highlight misclassified points.

<code>fig,axes = plt.subplots(1,2,figsize=(8,3.8),dpi=300)
decision_boundaries(rf, X, y, ax=axes[0], feature_names=['proline','flavanoid'])
decision_boundaries(rf, X, y, ax=axes[1],
    show=['instances','boundaries','misclassified'],
    feature_names=['proline','flavanoid'])
plt.show()
</code>

One‑Dimensional Boundary

We can also visualize the boundary using a single feature ( proline ).

<code>x = df1[['proline']].values
y = df1['target'].astype('int').values
rf = RandomForestClassifier(n_estimators=10, min_samples_leaf=10, n_jobs=-1)
rf.fit(x, y)
decision_boundaries(rf, x, y,
    feature_names=['proline'],
    target_name='wine_type',
    colors={'scatter_marker_alpha': .2},
    figsize=(5,1.5))
</code>

This demonstrates how to plot classification results, decision boundaries, and misclassifications for a Random Forest model.