## Modeling/predicting techniques per class:
2. Decision Trees
3. 
4. Linear Regression + Logistical Regression (numerical data)
5. 
6. Support Vector Machine  (categorical data)
7. K-means clustering and K-medoids clustering  (numerical data)





| testsize | 0.8   | 0.7   | 0.6   | 0.5   | 0.4   | 0.3   | 0.2 |  0.1  | 0.05 | 0.025| 0.01|
| -------- | ----- | ----- | ----- | ----- | ----- | ----- | --- | ----- | --| --|
| score    | 0.255 | 0.308 | 0.357 | 0.410 | 0.459 | 0.515 |  0.556 | 0.621| 0.658 | 0.677 | 0.692
|          |       |       |       |       |       |       |     |       |

Exemple:

|      | acc  | prec | rec  | f1   |
| ---- | ---- | ---- | ---- | ---- |
| non  | 0.65 | 0.7  | 0.72 | 0.71 |
| grid | 0.69 | 0.73 | 0.77 | 0.75 |
| rand | 0.64 | 0.69 | 0.71 | 0.70 |
|      |      |      |      |      |


Classifier:
|     | acc | prec | rec | f1  |
| --- | --- | ---- | --- | --- |
|     |     |      |     |     |
|     |     |      |     |     |
|     |     |      |     |     |
|     |     |      |     |     |