Statements (40)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
Machine learning algorithm
Regression algorithm |
| gptkbp:advantage |
Captures local structure in data
Computationally expensive for large datasets Sensitive to bandwidth selection |
| gptkbp:alsoKnownAs |
gptkb:LWR
LOESS LOWESS |
| gptkbp:application |
Data visualization
Curve fitting Smoothing |
| gptkbp:category |
gptkb:Supervised_learning
gptkb:Regression_analysis |
| gptkbp:characteristic |
Fits simple models to localized subsets of data
|
| gptkbp:developedBy |
gptkb:William_S._Cleveland
|
| gptkbp:field |
Statistics
Machine learning Data analysis |
| gptkbp:firstPublished |
1979
|
| https://www.w3.org/2000/01/rdf-schema#label |
Locally Weighted Regression
|
| gptkbp:input |
Weights
Data points Query point |
| gptkbp:method |
Assigns higher weights to points near the query
Fits local linear or polynomial models |
| gptkbp:output |
Predicted values
Smoothed curve |
| gptkbp:parameter |
Kernel function
Bandwidth |
| gptkbp:relatedTo |
gptkb:Linear_regression
Nonlinear regression Smoothing splines Kernel regression |
| gptkbp:software |
gptkb:Python_(statsmodels)
gptkb:MATLAB R |
| gptkbp:type |
Non-parametric regression
|
| gptkbp:uses |
Weighted least squares
|
| gptkbp:bfsParent |
gptkb:Chris_Loader
|
| gptkbp:bfsLayer |
8
|