Content Filter Engine

contentFilterEngine

The contentFilterEngine module is a comprehensive framework designed to implement various machine learning algorithms and techniques for content filtering and recommendation systems. It encompasses a wide range of approaches, including traditional machine learning algorithms, neural network-based models, probabilistic statistical methods, and hybrid ensemble methods.

Structure: - traditional_ml_algorithms: Implements classic machine learning algorithms such as Logistic Regression, SVM, Decision Trees, and more. - nn_based_algorithms: Contains neural network-based models like CNN, RNN, and Transformer, among others. - probabilistic_statistical_methods: Includes methods like LDA and LSA for statistical analysis and modeling. - embedding_representation_learning: Focuses on techniques like Word2Vec and Doc2Vec for learning vector representations of data. - performance_scalability: Addresses issues related to feature extraction and scalable algorithms. - fairness_explainability: Provides tools for ensuring fairness and explainability in machine learning models. - learning_paradigms: Explores advanced learning paradigms such as meta-learning, transfer learning, and zero-shot learning. - other_approaches: Covers additional methods like sentiment analysis and rule-based systems.

Usage: The contentFilterEngine is designed to be modular and extensible, allowing developers to easily integrate and experiment with different algorithms and techniques. Each submodule can be imported and utilized independently, providing flexibility in building custom content filtering solutions.

Example

from engines.contentFilterEngine.traditional_ml_algorithms import TRA_LR model = TRA_LR() model.fit(X_train, y_train) predictions = model.predict(X_test)

Note: - Ensure that all dependencies are installed and configured correctly before using the module. - The module is intended for educational and research purposes and may require further optimization for production use.