CODEWITHSUNDEEP
azureregressionazure-machine-learning-service
Atloka
Atloka

Reputation: 195

Azure Machine Learning Results Interpretation

I try to do an experiment in Azure Machine Learning whith a "Decision Forest Regression" Algorythm to predict Weather. enter image description here

I use the Weather Dataset that AML Studio suggested me (It's 400K rows of Wheater in a airport). enter image description here

I would like to predict the "DryBulbCelsus" column (it's values 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 colums of results "Score Label Mean" and "Score Label Standard Deviation" with data that I don't understand. enter image description here

If someone work with AML and can explain me how I must interprete the data in result. Thank you !

Upvotes: 5

Views: 1874

Answers (2)

Jimmy
Jimmy

Reputation: 2961

I was confused by this too, 'Scored Label Mean' is the mean of the scored labels from the different trees in the forest in a Decision Forest model, for example. And so is equivalent to the 'Scored Labels' output from a Liner Regression model, for example.

Upvotes: 0

Roope Astala - MSFT
Roope Astala - MSFT

Reputation: 756

Some learners, specifically the Decision Forest family and Bayes Point Machine, are capable of estimating the uncertainty around the prediction.

The "Scored Label Mean" is the prediction, and "Scored Label Standard Deviation" is the uncertainty around that prediction.

Upvotes: 6

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