Machine Learning for Mobile

What are the common pitfalls in machine learning projects?

The following are some of the common pitfalls seen in any machine learning project:

• Unrealistic objectives, unclear problem definition with no proper objectives
• Data problems:
  • Insufficient data to establish predictive patterns
  • Incorrect selection of predictor attributes
  • Data preparation problems
  • Data normalization problems—failure to normalize data across datasets
  • Bias in data use to solve the problem
• Inappropriate machine learning method selection:
  • The ML method selected doesn't suit the problem statement defined
  • Not trying alternative algorithms
• Giving up too soon. This happens very often. Engineers tend to lose interest if they don't see initial results and are unable to ...