Part 6 - Implementing Support Vector Machines (SVM) in Python

Machine Learning Algorithms Series - Classification with scikit-learn

This article explains how to implement Support Vector Machines (SVM) in Python for classification problems. It covers importing necessary libraries, preparing data, training the model, making predictions, and evaluating performance using accuracy scores and confusion matrices.

Introduction to Support Vector Machines (SVM)

Support Vector Machines (SVM) is a powerful classification algorithm that works by finding the hyperplane that best separates classes in the feature space. SVM aims to maximize the margin between the classes, making it a good choice for binary classification, especially when the classes are well separated.

Step-by-Step Implementation

1. Importing Libraries:

   • Import the SVC class (Support Vector Classifier) from sklearn.svm.

   • Import train_test_split from sklearn.model_selection to divide the dataset into training and testing sets.

   • Import accuracy_score and confusion_matrix from sklearn.metrics to evaluate the model.

   • Import numpy for numerical operations.

2. Preparing Data:

   • Create a NumPy array X representing the hours studied and prior grades of students.

   • Create a NumPy array y representing the outcomes (0 for fail, 1 for pass).

3. Splitting the Data:

   • Use train_test_split to divide the data into training and testing sets.

   • Specify test_size=0.2 to use 20% of the data for testing and 80% for training.

   • Set random_state=42 for reproducibility.

4. Initializing and Training the Model:

   • Create an instance of the SVC class, specifying the kernel type. For a linear SVM, use kernel='linear'. This attempts to find a linear hyperplane that best separates the two classes.

   • Train the model using the training data (X_train, y_train). The model learns the optimal hyperplane that separates the data points belonging to each class, maximizing the margin between them.

5. Making Predictions:

   • Use the trained SVM model to make predictions on the test data X_test.

   • The output y_pred contains the model's predictions (0 or 1).

6. Evaluating the Model:

   • Calculate the accuracy of the model by comparing the actual values y_test to the predicted values y_pred using accuracy_score.

   • Compute the confusion matrix to understand true positives, true negatives, false positives, and false negatives.

   • Print the accuracy and confusion matrix.

Complete Code Example

# Import necessary libraries
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix
import numpy as np

# Prepare data
X = np.array([[,], [,], [,], [,], [,], [,], [,], [,], [,], [,]])
y = np.array()

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize the SVM classifier
model = SVC(kernel='linear')

# Train the model
model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = model.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
confusion_matrix = confusion_matrix(y_test, y_pred)

# Print the results
print("Accuracy:", accuracy)
print("Confusion Matrix:\n", confusion_matrix)

Conclusion

This article demonstrates a complete implementation of the Support Vector Machine classifier, showing how it uses hours studied and prior grades to classify students as passing or failing. The SVM model is trained, tested, and evaluated using both accuracy and confusion matrix.