Source : https://towardsdatascience.com/how-are-logistic-regression-ordinary-least-squares-regression-related-1deab32d79f5
In linear regression, ε(random error term) here is the catch-all for whatever we are missing out by not measuring & capturing in the model[1].
This means there is scope for improvement and making the model less random. So, as we keep on including more independent variables (predictors/features) our model would have less random error.
Now, how is all this connected to destiny, you may say? Well, in that case, please watch the video at the top of the post again.
Here, Sadhguru[2] says that a lot of events were attributed to 'destiny/God's will' in the era gone by, as they didn't have access to science & technology in the scale we have access to, currently.
Now, we can pretty much point out causes of illness/accident/death/... to something known to us.
So, this is a measurement problem
In other words, "what's gets measured gets managed"
In essence, this is what modern machine learning & AI is also about. They keep on studying tons of data to come up with better models. What the model can't explain is error or catch-all or Destiny (if I may say so!)
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