Lets start by importing and exploring the Wine dataset from scikit-learn.
from sklearn.datasets import load_wine
# Load the wine dataset
wine_data = load_wine()
X = wine_data.data
y = wine_data.target
feature_names = wine_data.feature_names
target_names = wine_data.target_names
# print to check the overall structure of our dataset
# and also to find how many classes we have
print(f"Dataset dimensions: {X.shape}")
print(f"Target classes: {target_names}")Dataset dimensions: (178, 13)
Target classes: ['class_0' 'class_1' 'class_2']import pandas as pd
# Data preparation
df = pd.DataFrame(X, columns=feature_names)
df['target'] = y
df['target_names'] = df['target'].map({0: 'class_0', 1: 'class_1', 2: 'class_2'})
df.head()| alcohol | malic_acid | ash | alcalinity_of_ash | magnesium | total_phenols | flavanoids | nonflavanoid_phenols | proanthocyanins | color_intensity | hue | od280/od315_of_diluted_wines | proline | target | target_names | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 14.23 | 1.71 | 2.43 | 15.6 | 127.0 | 2.80 | 3.06 | 0.28 | 2.29 | 5.64 | 1.04 | 3.92 | 1065.0 | 0 | class_0 |
| 1 | 13.20 | 1.78 | 2.14 | 11.2 | 100.0 | 2.65 | 2.76 | 0.26 | 1.28 | 4.38 | 1.05 | 3.40 | 1050.0 | 0 | class_0 |
| 2 | 13.16 | 2.36 | 2.67 | 18.6 | 101.0 | 2.80 | 3.24 | 0.30 | 2.81 | 5.68 | 1.03 | 3.17 | 1185.0 | 0 | class_0 |
| 3 | 14.37 | 1.95 | 2.50 | 16.8 | 113.0 | 3.85 | 3.49 | 0.24 | 2.18 | 7.80 | 0.86 | 3.45 | 1480.0 | 0 | class_0 |
| 4 | 13.24 | 2.59 | 2.87 | 21.0 | 118.0 | 2.80 | 2.69 | 0.39 | 1.82 | 4.32 | 1.04 | 2.93 | 735.0 | 0 | class_0 |
from seaborn import pairplot
# Scatter plots - visualize potential correlations or patterns between features
# The diagonal of the pairplot displays the distribution of each feature
pairplot(df, hue = 'target', palette = 'Set2', diag_kind = 'kde')
# print to check the target classes distributions
print("\nTarget distribution:")
print(df['target_names'].value_counts())
Target distribution:
target_names
class_1 71
class_0 59
class_2 48
Name: count, dtype: int64# print to check if there are any missing data in the dataset
print(df.isnull().sum())alcohol 0
malic_acid 0
ash 0
alcalinity_of_ash 0
magnesium 0
total_phenols 0
flavanoids 0
nonflavanoid_phenols 0
proanthocyanins 0
color_intensity 0
hue 0
od280/od315_of_diluted_wines 0
proline 0
target 0
target_names 0
dtype: int64from sklearn.model_selection import train_test_split
# Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
print(f"Training set shape: {X_train.shape}")
print(f"Testing set shape: {X_test.shape}")Training set shape: (142, 13)
Testing set shape: (36, 13)from sklearn.tree import DecisionTreeClassifier
# Create and train a decision tree classifier with max depth of 3
tree = DecisionTreeClassifier(max_depth=3, criterion='gini', random_state=42)
tree.fit(X_train, y_train)DecisionTreeClassifier(max_depth=3, random_state=42)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
DecisionTreeClassifier(max_depth=3, random_state=42)
import matplotlib.pyplot as plt
from sklearn.tree import plot_tree
# Visualize the decision tree
plt.figure(figsize=(15, 8))
plot_tree(tree,
filled=True,
feature_names=feature_names,
class_names=target_names)
plt.title("Decision Tree")
plt.show()
Evaluate the model through various metrics and visualize the confusion matrix
from sklearn.metrics import confusion_matrix, classification_report,accuracy_score
import seaborn as sns
y_pred = tree.predict(X_test)
print(classification_report(y_test, y_pred, target_names=target_names)) precision recall f1-score support
class_0 1.00 0.93 0.96 14
class_1 0.88 1.00 0.93 14
class_2 1.00 0.88 0.93 8
accuracy 0.94 36
macro avg 0.96 0.93 0.94 36
weighted avg 0.95 0.94 0.94 36
cm = confusion_matrix(y_test, y_pred)
plt.figure(figsize=(6,4))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=target_names, yticklabels=target_names)
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.title('Confusion Matrix')
plt.show();
Accuracy measures how often the classifier makes the correct prediction:
\[ \text{Accuracy} = \frac{\text{Correct predictions}}{\text{Total predictions}} \]
# Train Decision Trees with different depths and record accuracy
max_depth_range = range(1, 10)
train_accuracies = []
test_accuracies = []
for depth in max_depth_range:
clf = DecisionTreeClassifier(max_depth=depth, random_state=42)
clf.fit(X_train, y_train)
# Predict on training and testing data
train_pred = clf.predict(X_train)
test_pred = clf.predict(X_test)
# Accuracy scores
train_acc = accuracy_score(y_train, train_pred)
test_acc = accuracy_score(y_test, test_pred)
train_accuracies.append(train_acc)
test_accuracies.append(test_acc)
# Plot training vs testing accuracy
plt.figure(figsize=(8, 5))
plt.plot(max_depth_range, train_accuracies, label='Training Accuracy', marker='o')
plt.plot(max_depth_range, test_accuracies, label='Testing Accuracy', marker='s')
plt.xlabel('Max Depth of Decision Tree')
plt.ylabel('Accuracy')
plt.title('Training vs Testing Accuracy')
plt.legend()
plt.grid(True)
plt.show()
As we can see, the decision tree performs fairly well on the wine data and the accuracy doesn’t change after max_depth > 3. This is because the wine data from scikit-learn is fairly simple and clean. However, this is not common in real life, where data are more complex and noisy.