4 Reasons Your Machine Learning Model is Wrong (and How to Fix It)

When we build these models, we always use a set of historical data to help our machine learning algorithms learn what is the relationship between a set of input features to a predicted output. We'll show how you can evaluate these issues by assessing metrics of bias vs. variance and precision vs. recall, and present some solutions that can help when you encounter such scenarios. If we were to train a machine learning model and it learned to always predict an email as not spam (negative class), then it would be accurate 99% of the time despite never catching the positive class. Similarly, increasing the number of training examples can help in cases of high variance, helping the machine learning algorithm build a more generalizable model.