The most critical and essential part of any machine learning model building is the phase of “Model Evaluation”. On average, a Data Scientist spends 60% of their time cleaning and organizing data to ensure better prediction power and performance rate of a model. But how do we know if our model is effective? How do we calculate the performance of the developed model?
Fig. 1: Machine Learning process flowWe need some evaluation metrics to measure performance and effectiveness. One such metric would be the Confusion Matrix. There are many classification metrics out there but we are more interested in the confusion matrix for now.
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Why misinterpreting these two concepts has serious implications?
“Correlation does not imply causation”- we all might have come across this statement but what does that actually mean? Correlation and causation are terms that are mostly misunderstood and often used interchangeably. I still remember my Probability and Statistics professor discussing, how important it is to know about the differences between the two terms back in college. These two terms have always found a way into my life, be it in research, at work, and recently while taking some data science classes. …
