Across
- 5. A bad choice of loss function for classifiation
- 7. Focus on samples that are hard to classify
- 8. Measure of linear relationship between two vectors
- 9. Allows for feature selection
- 10. Reduce variance & increase bias
- 12. Vector direction remains invariant to transformation
- 13. Variable inferred from other variable
- 14. One of the god fathers in AI
- 15. Test to measure the ability to exhibit human behviour
Down
- 1. In this all data points influence decision boundary
- 2. 'p' is the probablity of sucess & expected value of random variable is p
- 3. Map data from a lower dimension to a higher dimesnion
- 4. Magnification factor in a linear transformation
- 6. Decorrelates the feature space / Projected data's covariance matrix is a diagonal matrix
- 8. Global & local minima are the same
- 11. Measure of purity