Uses of Interface
org.pepstock.charba.client.ml.IsRegression
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Packages that use IsRegression Package Description org.pepstock.charba.client.ml Contains all classes to activate the ML library to use the regression capabilities. -
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Uses of IsRegression in org.pepstock.charba.client.ml
Classes in org.pepstock.charba.client.ml that implement IsRegression Modifier and Type Class Description classExponentialRegressionExponential regression is a non-linear regression technique that looks like this:
y = A * eB * x
classLinearRegressionSimple Linear Regression is a type of Regression algorithms that models the relationship between a dependent variable and a single independent variable.
The relationship shown by a Simple Linear Regression model is linear or a sloped straight line, hence it is called Simple Linear Regression.
The key point in Simple Linear Regression is that the dependent variable must be a continuous/real value.
However, the independent variable can be measured on continuous or categorical values.
The Simple Linear Regression model can be represented using the below equation:
y = a0 + a1x
where
a0 is the intercept of the Regression line (can be obtained putting x=0) a1 is the slope of the regression line, which tells whether the line is increasing or decreasingclassPolynomialRegressionPolynomial Regression is a regression algorithm that models the relationship between a dependent(y) and independent variable(x) as nth degree polynomial.
The Polynomial Regression equation is given below:
y = b0 + b1x1 + b2x12 + b2x13 +......classPowerRegressionPower regression is a non-linear regression technique that looks like this:
y = A * xBclassRobustPolynomialRegressionRobust regression is a form of regression analysis designed to overcome some limitations of traditional parametric and non-parametric methods.
Regression analysis seeks to find the relationship between one or more independent variables and a dependent variable.classTheilSenRegressionIn TheilSen Regression, the estimation of the model is done by calculating the slopes and intercepts of a subpopulation of all possible combinations of subsample points.
The final slope and intercept is then defined as the spatial median of these slopes and intercepts.
The TheilSen Regression model can be represented using the below equation:
y = a0 + a1x
where
a0 is the intercept of the Regression line (can be obtained putting x=0) a1 is the slope of the regression line, which tells whether the line is increasing or decreasing
This regression can be computed efficiently, and is insensitive to outliers.
It can be significantly more accurate than non-robust simple linear regression (least squares) for skewed and heteroskedastic data, and competes well against least squares even for normally distributed data in terms of statistical power.
It has been called "the most popular nonparametric technique for estimating a linear trend".
See details on Theil–Sen estimator.Methods in org.pepstock.charba.client.ml with type parameters of type IsRegression Modifier and Type Method Description static <T extends IsRegression>
TIsRegression. checkAndGetIfValid(T regression)Checks if regression passed as argument is notnulland if consistent as well.
If not, throw aIllegalArgumentException, otherwise it returns the regression.Methods in org.pepstock.charba.client.ml that return IsRegression Modifier and Type Method Description static IsRegressionRegressionBuilder. build(IsRegression regression)Creates new regression instance, using the passed regression instance.static IsRegressionRegressionBuilder. build(RegressionDescriptor descriptor)Creates new regression, using the passed regression descriptor instance to create new regression.IsRegressionRegressionDataset. getRegression()Returns the regression instance, applied to the dataset.Methods in org.pepstock.charba.client.ml with parameters of type IsRegression Modifier and Type Method Description static IsRegressionRegressionBuilder. build(IsRegression regression)Creates new regression instance, using the passed regression instance.static voidIsRegression. checkIfValid(IsRegression regression)Checks if regression passed as argument is notnulland if consistent as well.
If not, throw aIllegalArgumentException.static RegressionBuilderRegressionBuilder. create(IsRegression regression)Creates new regression builder, using the passed regression instance.
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