Data scientists perform model selection by:
Cross-Validation: Using techniques like k-fold cross-validation to assess model performance.
Hyperparameter Tuning: Adjusting model parameters to improve accuracy and performance.
Metrics Evaluation: Selecting appropriate metrics for evaluating model performance (e.g., accuracy, precision, recall).
In Kaggle competitions, model selection often involves extensive experimentation, feature engineering, and ensemble methods to achieve the best possible performance. Kaggle may place more emphasis on creative feature engineering and blending different models compared to traditional industry practices.