Entropy

Training and Evaluating Machine Learning Models

Once a model is selected, it must be trained using labeled data (for supervised learning) or structured data patterns (for unsupervised learning). The training process involves feeding input data into the model, adjusting weights, and minimizing error through optimization techniques like Gradient Descent.

Model evaluation is crucial to measure its accuracy, precision, recall, and F1-score using metrics like confusion matrices, ROC-AUC curves, and cross-validation. Ensuring a model generalizes well to unseen data is essential for real-world applications.