As i was practicing predictive modelling,do i need to undrestand statistics involved in machine learning algorithms?
Statistics will help you understand your output of the machine learning algorithm without statistics you will be able to see the output but you wont be able to learn the algorithm in depth. So i suggest statistics is important for any machine learning technique.
I also thought if stats concept are really required . While I was going through stats concepts , i could not realize importance of practical value of chi square , F test or ANOVA. But as I am progressing on model building, it is becoming clear that a lot of underlying is stats and I now have to keep looking back to stats concepts for relevance in model building. So do not ignore stats concepts.
Statistics are really important for building the base strong in ML. It is vital to understand the stats keywords to understand the algorithms, their output analysis and hypothesis testing. Below statistic topics are essential to know for any data scientists:
Variance and Standard Deviation.
Central Limit Theorem,
Bayes Theorem, confusion Matrix, Probability Distributions, Hypothesis testing errors, t, f-test, ANOVA.
Understanding the above concepts would be enough to kick off Machine Learning algorithms!
yeah thanks for all suggestions