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@ -137,22 +137,24 @@ The result will be the model's predictions for the entire dataset.
2. Bernoulli Naive Bayes
3. Gaussian Naive Bayes
8. ***Support Vector Classification***
9. ***K-Means***
10. ***k-Nearest Neighbors***
11. ***Outlier Finder (Using z-scores)***
12. ***Matrix Decompositions***
1. Primal Formulation (Hinge Loss Objective)
2. Dual Formulation (Via Lagrangian Multipliers)
10. ***K-Means***
11. ***k-Nearest Neighbors***
12. ***Outlier Finder (Using z-scores)***
13. ***Matrix Decompositions***
1. SVD Decomposition
2. Cholesky Decomposition
- Positive Definiteness Checker
3. QR Decomposition
13. ***Numerical Analysis***
14. ***Linear Algebra Module***
15. ***Statistics Module***
16. ***Data Processing Module***
14. ***Numerical Analysis***
15. ***Linear Algebra Module***
16. ***Statistics Module***
17. ***Data Processing Module***
1. Setting and Printing Datasets
2. Feature Scaling
3. Mean Normalization
4. One Hot Representation
S
5. Reverse One Hot Representation
17. ***Utilities***
1. TP, FP, TN, FN function