Model comparison and selection

After we are done with the model tuning part, we would definitely want to repeat the whole modeling part for models other than the current one in the hope that we might get better results. As ML practitioners, it is our job to ensure that the model we have finally come up with is better than the other ones (obviously in various aspects). Naturally, comparing different ML models is a time-consuming task and we may not be able to always afford to do this when we need to meet short deadlines. In cases like this, we incorporate the following aspects of an ML model: 

• Explainability, which answers a given question (how interpretable is the model and how easily it can be explained and communicated?)
• In-memory versus out-of-memory modeling
• The number of features and instances in the dataset
• Categorical versus numerical features
• The nonlinearity of the data
• Training speed
• Prediction speed