Evaluation Metrics for Regression and Classification Models

1. 1Build a regression model using a dataset of choice
2. 2Calculate mean squared error (MSE) and mean absolute error (MAE) to evaluate regression model performance
3. 3Configure a classification model and calculate accuracy, precision, and recall to evaluate its performance
4. 4Test the models using cross-validation to ensure reliability
5. 5Apply the learned metrics to compare and contrast different models

Data scientists and machine learning engineers benefit from understanding evaluation metrics to refine their models and improve performance, while product managers can use these metrics to inform decision-making

💡 Choosing the right evaluation metric is crucial for model performance and reliability

📊 Evaluate your ML models with the right metrics! 🚀