Supervised Numeric-Value Prediction Task
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(Redirected from regression analysis)
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 applies 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]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]K[/math] such that w_{i} = K(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. ...