Training and Tuning

Model Training and Evaluation image concept

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.