# Supervised Numeric-Value Prediction Task

(Redirected from Supervised Point Estimation Task)

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A supervised numeric-value prediction task is a data-driven point estimation task that is a supervised estimation task.

**Context:****Input:**a Numerically Labeled Training Dataset.**output:**an Predicted Numeric Value.- optional output: a Trained Estimation Function.

**Performance Metric**: Numeric-Value Prediction Performance Metric, such as RMSE.- It can be solved by a Supervised Numeric Prediction System (that implements a supervised point estimation algorithm).
- It can range from being a Supervised Model-based Numeric Prediction Task (such as linear regression) to being a Supervised Instance-based Point Estimation Task (such as k-nearest regression).
- It can range from being a Fully-Supervised Regression Task to being a Semi-Supervised Regression Task.
- It can range from being a Univariate Regression Task to being a Multivariate Regression Task (with more than one dependent variable).

**Example(s):**- predict the orbit of stellar objects based on past history (like Gauss did on ~1805).
- predict Boston housings sales price based on a (Harrison & Rubinfeld, 1978) dataset, such as sklearn's Boston dataset.
- a (Fanaee-T & Gama, 2014) Prediction Task.
- …

**Counter-Example(s):****See:**Function Fitting; Residual; Kernel Function; Point Estimator; Gaussian Processes; Linear Regression; Support Vector Machines; Regression Analysis.

## References

### 2011

- (Quadrianto & Buntine, 2011) ⇒ Novi Quadrianto; Wray I. Buntine. (2011). “Regression.” In: (Sammut & Webb, 2011) p.838

### 1997

- (Mitchell, 1997) ⇒ Tom M. Mitchell. (1997). “Machine Learning." McGraw-Hill.
- QUOTE: Much of the literature on nearest-neighbor methods and weighted local regression uses a terminology that has arisen from the field of statistical pattern recognition. ...
*Regression*means approximating a real-valued target function.*Residual*is the error*f*(^{^}*x*) - [math]\displaystyle{ f }[/math](*x*) in approximating the target function.*Kernel function*is the function of distance that is used to determine the wight of each training example. In other words, the kernel function is the function [math]\displaystyle{ K }[/math] such that*w*= K_{i}*(*d*(*x_{i}*,*x_{q}*)).*

- QUOTE: Much of the literature on nearest-neighbor methods and weighted local regression uses a terminology that has arisen from the field of statistical pattern recognition. ...