Essential Machine Learning Algorithms and Metrics
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Evaluation Metrics for ML Models
Accuracy: The ratio of correctly predicted instances.
Precision: Correct positive predictions divided by total predicted positives.
Recall: Correct positive predictions divided by actual positives.
F1 Score: The harmonic mean of precision and recall.
K-Nearest Neighbors (KNN) Algorithm
A classification algorithm that works by finding the 'k' closest training examples to a data point.
Strengths: Simple to understand, effective for smaller datasets.
Weaknesses: Sensitive to irrelevant features and the scale of the data.
Applications: Image recognition, recommendation systems.
Ensemble Learning Techniques
Combines multiple models to improve predictive performance.
Methods:
- Bagging (e.g., Random Forests)
- Boosting (e.g., AdaBoost)