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I tried to do an experiment in Azure Machine Learning with a "Decision Forest Regression" Algorithm to predict Weather. I used the Weather Dataset that AML Studio suggested to me (It's 400K rows of Wheater in an airport).

I would like to predict the "DryBulbCelsus" column (it's valued between 20 and 23), so I select the column in the Train Model. I run it everything goes well. But the problem is that I don't understand my score model. I have 2 more columns of results "Score Label Mean" and "Score Label Standard Deviation" with data that I don't understand. 

If someone works with AML and can explain to me how I must interpret the data in the result.

1 Answer

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Well, standard deviation means the number of results that are different from the actual predicted one. 

Score Label mean is the actual predicted result.  

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