Just a small clarification: ML generates estimated probabilities. Folks unfamiliar with ML assume those probabilities are good to use – e.g. 10% = 1 out of 10 times, it will definitely work. The goodness of estimates comes down to the quality of input data, the appropriateness of the algorithm, and finally, the background / operating context (which are no directly captured but implied through the input data).
So ultimately, it comes down to the probability that a probability works! :)
Just a small clarification: ML generates estimated probabilities. Folks unfamiliar with ML assume those probabilities are good to use – e.g. 10% = 1 out of 10 times, it will definitely work. The goodness of estimates comes down to the quality of input data, the appropriateness of the algorithm, and finally, the background / operating context (which are no directly captured but implied through the input data).
So ultimately, it comes down to the probability that a probability works! :)
Yes, you are right I missed out on the "estimated" keyword. Thanks for pointing it out. :)