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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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