R² is a widely used measure of fit, but for many analysts, it is just a number. They believe high R² ➡ Good predictions. This is not always true! Now I will clarify. 🔽 R-squared measures how well the regression model fits the observed data. To be more precise: It is the proportion of the variation in the dependent variable that is predictable from the independent variable. It usually ranges from 0 to 1: (In rare cases it can be negative, I will explain this in another tweet) R² = 0 The model does not explain any of the variability in the dependent variable ➡ No predictive power ➡ Bad model. R² = 1 The model perfectly explains all the variability in the dependent variable ➡ Perfect fit to the data ➡ Good model if not overfitted and has predictive power. A high R-squared value does not mean that the predictions made by the model will be correct. It doesn't measure predictability power, it measures how well the model fits! In the example below, we compare the mean of the data to a fitted line. Of course, the mean of values is not a good fit ➡ the errors are large. On the other hand, the fitted line has smaller errors ➡ The R² will be close to 1. To calculate R² we need: - The total sum of squares for the mean - Sum of squares for the residuals from the model - Finally, subtract the ratio from 1 ___ That's it for today. I hope you've found this Tweet helpful. Like/Retweet for support and follow @levikul09 for more Data Science content. Thanks 😉

Hi All, It's been a while... I turned off content creation a few months ago and had some much-needed time away from it. Several reasons lead to the decision: - I started to work on an interesting project beside my 9-5. That took priority. I may talk about this later as it is data-related. - I had enough of it. I created content daily for 2.5 years and started chasing the algorithm of X. I burnt out. I realized that I am not creating content for enjoyment and to learn, but to farm engagement. It was more like learning marketing or copywriting, not Data. I tried to move to a newsletter, but I realized that it was not the platform. It was me. BUT I liked to do it and I knew I will be back. I feel like it's time. I had some good rest and I feel the motivation again. I won't be here on X every day. The main focus will be Data Ground Up (Link in bio) and maybe video(?!) We will see. But sometimes I will pop up here as well. I look at it as starting from zero. I need to figure things out again. I will try to do stuff that is cool to me and every once in a while you may also like it. If you are in, then go to Data Ground Up!