Points to consider while building a Machine Learning model

The objective of this post is to list down some of the pointers to keep in mind while building a Machine Learning model.

• Always start with the simplest of models. You can increase the complexity if the performance of a simple model is inadequate.
• Understand your dataset first. 
• Build a baseline model before building any prediction model. I will expand on this further in another post.
• Complex models tend to over fit and simpler models tend to under fit. It is your job to find a balance between these two.
• High bias and low variance - A property of simpler models. Suggests under fitting.
• High variance and low bias - A property of complex models. Suggests over fitting.
• In any Machine Learning model, if the number of parameters is greater than the number of training examples, beware. It leads to over fitting. Try considering a simpler model with lesser number of parameters or reduce the number of hidden layers or anything else to reduce the number of parameters of the model.
• Always normalize the inputs. Neural Networks are optimized for working on numbers between 0 and 1. Any number greater than 1 leads to explosive gradient descent, which involves weight updates by large numbers.
• Regularization is very very important. Therefore, consider using an XGBoost model instead of Random Forest.

Some terms to keep in mind:

• Stratified Sampling - When the training data is overly skewed, the practice of picking the samples such the final training data has the distribution you need.
• Bootstrapping - Evaluating the same model with different random seeds.