Machine learning Model Pipeline: Model building

We can build various models like linear models. for eg: linear regression models(MARS). We can build decision tree based models like random forest, gradient boosted trees. We can also build neural networks. We can also build clustering algorithms.
And then when we pass the pre processed data to the model, we get the prediction that they make.



We need to then evaluate the performance of
predictions that this model make.

For classification we can measure the ROC-AUC which gives us the indication that how many times the model makes good assessment
vs how many times the model makes wrong assessment.





Some time we also build multiple ml algorithms and then build the meta model that takes in prediction  of all the initial models and combines them to make better assessment of the target. This is called Meta Ensembling.



We can have this also as a pipeline of Meta ensembling for model deployment.