Challenges in machine learning

Some of the challenges we face in machine learning are as follows:

• Lack of a well-defined machine learning problem. If the problem is not defined clearly as per the definition with required criteria, the machine learning problem is likely to fail.
• Feature engineering. This relates to every activity with respect to data and its features that are essential for the success of the machine learning problem.
• No clarity between the training set and test set. Often the model performs well in the training phase, but fails miserably in the field due to a lack of all possible data in the training set. This should be taken care of for the model to succeed in the field.
• The right choice of algorithm. There is a wide range of algorithms available, but which one suits our problem best? This should be chosen properly in the iteration with proper parameters required.