Try a walking desk to stay healthy while you study or work! Full notes at  ocdevel.com/mlg/15  Concepts Performance Evaluation Metrics: Tools to assess how well a machine learning model performs tasks like spam classification, housing price prediction, etc. Common metrics include accuracy, precision, recall, F1/F2 scores, and confusion matrices. Accuracy: The simplest measure of performance, indicating how many predictions were correct out of the total. Precision and Recall: Precision: The ratio of true positive predictions to the total positive predictions made by the model (how often your positive predictions were correct). Recall: The ratio of true positive predictions to all actual positive examples (how often actual positives were captured). Performance Improvement Techniques Regularization: A technique used to reduce overfitting by adding a penalty for larger coefficients in linear models. It helps find a balance between bias (underfitting) and variance (overfitting). Hyperparameters and Cross-Validation: Fine-tuning hyperparameters is crucial for optimal performance. Dividing data into training, validation, and test sets helps in tweaking model parameters. Cross-validation enhances generalization by checking performance consistency across different subsets of the data. The Bias-Variance Tradeoff High Variance (Overfitting): Model captures noise instead of the intended outputs. It's highly flexible but lacks generalization. High Bias (Underfitting): Model is too simplistic, not capturing the underlying pattern well enough. Regularization helps in balancing bias and variance to improve model generalization. Practical Steps Data Preprocessing: Ensure data completeness and consistency through normalization and handling missing values. Model Selection: Use performance evaluation metrics to compare models and select the one that fits the problem best.

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