I am trying to use basic classification algorithms in Python using sklearn. Unfortunately my dependent variable in training data are probabilities of whether it will rain or not. I know how to do this if my dependent variable is "rain" or "sunny". But now it is a probability. Can I use sklearn classification algorithms for this problem or should I use regression? Please advise.
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1$\begingroup$ After googling a bit, I realized that I could convert the [0,1] probability into a normally distributed variable using log (1 / (1 - x)) transformation and then run the regressions. I am still unsure how to solve this as a classification problem. $\endgroup$ – pavybez Dec 23 '16 at 11:36
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1$\begingroup$ If you have the base counts the probabilities came from (the denominator of the prob calculation), you can easily alter data to use classification algorithms. $\endgroup$ – B_Miner Jan 12 '17 at 15:55
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1$\begingroup$ Otherwise you could try what you suggest, but Beta regression is developed specifically for this type of problem. $\endgroup$ – B_Miner Jan 12 '17 at 15:57
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I do not see any chance to model a continuous output between [0,1] as a classification problem.
Use regression with P('Sunny') as the target variable.
