Different types of ML models, how they work, differences among them. Types of performance metrics, pros and cons of them, etc.

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Different types of ML models, how they work, differences among them, types of performance metrics, pros and cons.

Types of ML models include linear regression, decision trees, support vector machines, neural networks, etc.
ML models work by learning patterns from data to make predictions or decisions.
Differences among models include complexity, interpretability, scalability, and performance.
Performance metrics for ML models include accuracy, precision, recall, F1 score, ROC curve, etc.
Pros of performance metrics: provide quantitative evaluation of model performance. Cons: may not capture all aspects of model performance.
Examples: Linear regression for regression tasks, decision trees for classification tasks, neural networks for complex patterns.
Linear regression: simple, interpretable, but limited in capturing complex relationships. Neural networks: complex, powerful, but black box.
Accuracy: measures overall correctness of predictions. Precision: measures proportion of true positive predictions among all positive predictions.

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