Training and Tuning

Model Training

• Train the selected or developed model using the prepared dataset.
• Adjust parameters during training to enhance model performance.

Validation and Testing

• Validate the trained model on a separate dataset.
• Evaluate model accuracy, precision, recall, and other relevant metrics.

Hyperparameter Tuning

• Optimize model performance through systematic hyperparameter tuning.
• Use techniques like grid search or randomized search for parameter exploration.

Validation and Testing

• Validate the model on a separate dataset to assess its generalization performance, and conduct thorough testing to evaluate accuracy and effectiveness.

Iterative Development

• Adopt an iterative development approach, incorporating feedback from testing to refine and improve the model over successive iterations.