The Model Building phase follows the Preprocessing phase, where data is organized and prepared for analysis. This phase focuses on selecting and setting up the appropriate machine learning models to solve the problem at hand.
Key Steps
Types of Models:
- Choose a model to apply based on the problem requirements and data characteristics.
- Explore different Machine Learning Algorithms to find the best fit for your data.
- Consider the tradeoffs between parametric vs nonparametric models.
Setting Up a Model:
- Divide the data into TrainDevTest Sets to ensure robust evaluation and tuning.
- Optimize Model Parameters and configurations for best performance.
Model Selection:
- Evaluate the appropriateness of models in the Model Selection phase.