Difference between revisions of "Fast Forest Quantile Regression"
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* [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]] | * [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]] | ||
Revision as of 07:50, 4 July 2023
YouTube search... ...Google search
- AI Solver
- Regression Analysis
- Math for Intelligence ... Finding Paul Revere ... Social Network Analysis (SNA) ... Dot Product ... Kernel Trick
- Fast Forest Quantile Regression | Microsoft
- 5 Regression Loss Functions All Machine Learners Should Know - Choosing the right loss function for fitting a model | Prince Grover
- Loss Functions
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..
