3. Choosing the Right Model for Training

Selecting the appropriate model for training depends on several factors, including the type of problem (classification, regression, clustering), the structure of the data, and computational resources available. The choice of model can significantly impact performance and generalization ability. Some common models include:

• Linear Regression: Best suited for regression problems where the relationship between variables is approximately linear.
• Decision Trees and Random Forests: Useful for both classification and regression.
• Support Vector Machines (SVM): Effective for classification tasks with clear decision boundaries.
• Neural Networks: Well-suited for complex, high-dimensional data such as images and text.
• Gradient Boosting Methods: Powerful models for structured data.