Understanding SVM Predictions with Scikit-Learn’s Wine Dataset

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Support Vector Machines (SVMs) are a powerful supervised learning algorithm often used for classification tasks. In this blog, we’ll walk through an example using scikit-learn’s built-in Wine dataset and see how the SVC classifier behaves on a small slice of the data.

The Code

Here’s the Python code snippet we’re analyzing:

from sklearn.datasets import load_wine
from sklearn.svm import SVC

# Load dataset
X, y = load_wine(return_X_y=True)

# Train SVM model
clf = SVC(random_state=0).fit(X, y)

# True labels for samples 40 to 44
print(y[40:45])

# Predictions for those samples
print(clf.predict(X[40:45, :]))

# Accuracy score on this slice
print(clf.score(X[40:45, :], y[40:45]))

Step 1: The Dataset

  • load_wine() loads the Wine dataset (178 samples, 13 features, 3 classes: 0, 1, 2).

  • X → features (chemical analysis of wines).

  • y → target labels (wine classes).

When we print y[40:45], we get:

[0 0 0 0 0]

So, the true classes of samples 40–44 are all class 0.

Step 2: The Model

We train an SVM classifier (SVC) with default settings (kernel='rbf', C=1.0, gamma='scale').

This means it will try to separate the three wine classes using a non-linear decision boundary.

Step 3: Predictions

We now predict on the same slice:

clf.predict(X[40:45, :])

Output:

[2 0 0 2 0]

So the SVM predicts:

  • Sample 40 → predicted as class 2 (wrong).

  • Sample 41 → predicted as class 0 (correct).

  • Sample 42 → predicted as class 0 (correct).

  • Sample 43 → predicted as class 2 (wrong).

  • Sample 44 → predicted as class 0 (correct).

Step 4: Accuracy

We now check the accuracy on this slice:

clf.score(X[40:45, :], y[40:45])

Accuracy is computed as:

Accuracy=Number of Correct PredictionsTotal Predictions\text{Accuracy} = \frac{\text{Number of Correct Predictions}}{\text{Total Predictions}}

Here:

  • Correct predictions = 3 (indices 41, 42, 44).

  • Total predictions = 5.

Accuracy=35=0.6\text{Accuracy} = \frac{3}{5} = 0.6

Final Output

The three print statements give:

[0 0 0 0 0]
[2 0 0 2 0]
0.6

Key Takeaways

  • SVC with default parameters may misclassify some samples, especially in small subsets.

  • The true labels [0 0 0 0 0] show all wines belong to the same class, but the model sometimes predicts class 2.

  • Accuracy on this slice = 60%.

  • This example highlights why tuning hyperparameters (C, gamma, kernel) is important for SVMs.

✨ In summary, SVM is a robust classifier, but small test slices can reveal misclassifications. Always evaluate performance on the full dataset with cross-validation for more reliable insights.

Would you like me to extend this blog by showing how accuracy improves after hyperparameter tuning (GridSearchCV) for the SVM on the full Wine dataset?

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