Tools learned in the Andrew Ng Coursera Machine Learning course offered by Stanford. (Octave Gnu)
| Algorithm | Score |
|---|---|
| Support Vector Machine | 0.78947 |
| Logistic Regression | 0.76315 |
| Neural Network: | |
| - 7x7 lambda 0.005 | 0.75358 |
| - 7x2 lambda 0.3 | 0.77990 |
Tools from Machine Learning A-Zª: Hands-On Python & R In Data Science (Python, Jupyter Notebooks)
- Kernel Support Vector Machine - scores:
- linear: 0.76555
- rbf: 0.78229
- poly: 0.76794
- sigmoid: 0.63875
- Decision tree - scores:
- entropy: 0.72488
- gini: 0.70574