ML Algorithms -Concept

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Across
  1. 6. Predicts continuous values based on input features.
  2. 8. Focuses on the proportion of true positives among predicted positives.
  3. 9. Vectors that are the closest data points to the hyperplane in SVM.
  4. 10. The decision boundary used in SVM to separate classes.
  5. 11. The function used in logistic regression to map predictions to probabilities.
  6. 12. The first part of the name of a popular Python machine learning library.
Down
  1. 1. Measures the proportion of correct predictions in classification.
  2. 2. Categorizes data into distinct classes using decision boundaries.
  3. 3. A function used in SVM to handle non-linear decision boundaries.
  4. 4. A matrix summarizing TP, TN, FP, and FN in classification.
  5. 5. The distance between the hyperplane and the nearest data points in SVM.
  6. 7. Measures the ability to identify actual positives in classification.