Machine learning Fundamentals

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Across
  1. 2. Simplest ML algorithm, finds closest points.
  2. 3. Process of adjusting weights in neural networks.
  3. 8. Training with labeled data.
  4. 11. Model that separates data using a hyperplane (abbr.).
  5. 12. A model's error on unseen data.
  6. 13. Data feature used for training a model.
Down
  1. 1. Training with unlabeled data.
  2. 4. Mathematical function that reduces error.
  3. 5. Neuron-inspired algorithm for deep learning.
  4. 6. Performance metric: True Positives / (True Positives + False Positives).
  5. 7. Model that uses a tree structure for decisions.
  6. 9. Evaluation metric: (Precision + Recall)/2.
  7. 10. Technique to reduce model variance using multiple learners.