# 5.2.1 Suggestion Lists

## 5.2.1.1 Model-Specific

The following features are selected by the user:

* **Interaction**
* **Parametric**
* **Algorithmic complexity**

According to the score table, we can get:

|                           | Interaction | Parametric | Algorithmic complexity | SUM | AVERAGE | SUGGESTION |
| ------------------------- | ----------- | ---------- | ---------------------- | --- | ------- | ---------- |
| GLM                       | 1           | 1          | 1                      | 3   | 2       | STRONG     |
| Gradient Boosting         | 0.7         | 1          | 0.8                    | 2.5 | 2       | STRONG     |
| XGBoost                   | 0.7         | 0.9        | 0.7                    | 2.3 | 2       | STRONG     |
| Distributed Random Forest | 0.7         | 1          | 0.9                    | 2.6 | 2       | STRONG     |
| Deep Learning             | 0           | 0.1        | 0.2                    | 0.3 | 2       | LIGHTLY    |
| K-means                   | 0.1         | 0.7        | 0.5                    | 1.3 | 2       | MEDIUM     |

Therefore, the Model-Specific methods we suggest using are:

* **GLM**
* **Gradient Boosting**
* **XGBoost**
* **Distributed Random Forest**

## 5.2.1.2 Model-Agnostic

The following features are selected by the user:

* **Model internals**
* **Datapoint**
* **Detailed**
* **Certainty** 

According to the score table, we can get:

|                                                | Model internals | Datapoint | Detailed | Certainty | SUM | AVERAGE | SUGGESTION |
| ---------------------------------------------- | --------------- | --------- | -------- | --------- | --- | ------- | ---------- |
| Variable Importance / Standardized Coefficient | 1               | 0.5       | 1        | 1         | 3.5 | 2.6     | STRONG     |
| PDP                                            | 0.1             | 0.9       | 0.5      | 0.8       | 2.3 | 2.6     | MEDIUM     |
| ICE                                            | 0.2             | 1         | 1        | 0.9       | 3.1 | 2.6     | STRONG     |
| ALE                                            | 0               | 0.9       | 0.6      | 0         | 1.5 | 2.6     | LIGHTLY    |

Therefore, the Model-Agnostic methods we suggest using are

* **Variable Importance**
* **PDP**
* **ICE**