|
gptkbp:instanceOf
|
gptkb:statistical_analysis
|
|
gptkbp:assesses
|
gptkb:Mean_Squared_Error
gptkb:Root_Mean_Squared_Error
R-squared
Adjusted R-squared
|
|
gptkbp:assumes
|
Linearity
Homoscedasticity
Independence of errors
Normality of errors
|
|
gptkbp:category
|
Supervised Learning
Parametric Model
Regression Model
|
|
gptkbp:detects
|
gptkb:Outliers
Collinearity
Influential Points
|
|
gptkbp:estimatedCost
|
Relationship between variables
|
|
gptkbp:fittedBy
|
gptkb:Ordinary_Least_Squares
Gradient Descent
|
|
gptkbp:hasEquation
|
y = β0 + β1x + ε
|
|
gptkbp:hasType
|
Multiple Linear Regression
Simple Linear Regression
|
|
gptkbp:implementedIn
|
gptkb:Python
gptkb:SAS
gptkb:MATLAB
gptkb:SPSS
gptkb:Excel
R
|
|
gptkbp:input
|
One or more Independent Variables
|
|
gptkbp:introduced
|
gptkb:Francis_Galton
gptkb:Karl_Pearson
|
|
gptkbp:limitation
|
Sensitive to Outliers
Cannot model non-linear relationships without transformation
Affected by Multicollinearity
Assumes Linear Relationship
Assumes no autocorrelation in errors
|
|
gptkbp:output
|
Continuous Variable
|
|
gptkbp:reduces
|
Sum of Squared Errors
|
|
gptkbp:relatedTo
|
gptkb:Lasso_Regression
gptkb:Polynomial_Regression
gptkb:Ridge_Regression
Logistic Regression
|
|
gptkbp:requires
|
Quantitative Variables
|
|
gptkbp:usedFor
|
gptkb:Regression_Analysis
Predictive Modeling
|
|
gptkbp:usedIn
|
gptkb:Machine_Learning
gptkb:Data_Science
Statistics
|
|
gptkbp:bfsParent
|
gptkb:Regression_Models
|
|
gptkbp:bfsLayer
|
7
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Linear Regression
|