SVM

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svm.py

This module provides a basic implementation of the Support Vector Machine (SVM) algorithm, which is used for classification and regression tasks. SVMs are effective in high-dimensional spaces and are versatile due to the use of different kernel functions.

Usage

Import the SVM class and create an instance with the desired kernel and parameters. Fit the model to your data and use it for predictions.

Example: from engines.contentFilterEngine.traditional_ml_algorithms.svm import SVM model = SVM(kernel='linear') model.fit(X_train, y_train) predictions = model.predict(X_test)

Note

• The current implementation is a placeholder and needs to be completed with actual SVM logic.
• Consider using libraries like scikit-learn for a more comprehensive SVM implementation.