Difference between revisions of "Fast Forest Quantile Regression"
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* [[AI Solver]] | * [[AI Solver]] | ||
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* [http://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/fast-forest-quantile-regression Fast Forest Quantile Regression | Microsoft] | * [http://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/fast-forest-quantile-regression Fast Forest Quantile Regression | Microsoft] | ||
Revision as of 15:49, 2 June 2018
Quantile regression is useful if you want to understand more about the distribution of the predicted value, rather than get a single mean prediction value. This method has many applications, including:
- Predicting prices
- Estimating student performance or applying growth charts to assess child development
- Discovering predictive relationships in cases where there is only a weak relationship between variables
This regression algorithm is a supervised learning method, which means it requires a tagged dataset that includes a label column. Because it is a regression algorithm, the label column must contain only numerical values..
