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