Model Training and Evaluation in Data Science
Building a machine learning model involves multiple steps, including selecting an appropriate algorithm, training the model, and evaluating its performance. The typical workflow consists of:
- Splitting Data – Dividing the dataset into training, validation, and test sets. Common splits are 80-20 or 70-15-15.
- Model Selection – Choosing an appropriate algorithm based on the problem type (classification, regression, clustering).
- Hyperparameter Tuning – Optimizing parameters using techniques like Grid Search, Random Search, or Bayesian Optimization.
- Model Evaluation – Using performance metrics such as:
- Classification: Accuracy, Precision, Recall, F1-score, ROC-AUC
- Regression: RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), R² score
- Cross-Validation – Ensuring model robustness using techniques like K-Fold Cross-Validation.
Libraries like Scikit-learn, TensorFlow, and XGBoost provide powerful tools for training and evaluating models effectively.
