What is the issue and how can you solve for it?

Your team has developed a machine learning model for your customer. The test results indicate very strong predictive capability. The model is then deployed in production. Evaluation of the predictions in production show that they are off by a pronounced margin.

What is the issue and how can you solve for it?
A . The model is under fitted. Train with less data.
B . The model is over fitted. Add more features to the model to fix it.
C . The model is fine since the test results are good. Fix the production of incoming data.
D . The model is overfitted. Train with more data.

View Answer

Answer: D

Explanation:

If our ML model does well on the training set than on the production set, then we’re likely over fitting. Training with more data would be one solution.