Ridge Regression

Ridge Regression

Ridge Regression is located under Machine Leaning (  ) under Regression, in the left task pane. Use the drag-and-drop method to use the algorithm in the canvas. Click the algorithm to view and select different properties for analysis.

Refer to Properties of Ridge Regression.



Properties of Ridge Regression

The available properties of Ridge Regression are as shown in the figure given below.


The table given below describes the different fields present on the properties of Ridge Regression.

Field

Description

Remark

RunIt allows you to run the node.-
ExploreIt allows you to explore the successfully executed node.-
Vertical Ellipses

The available options are

  • Run till node
  • Run from node
  • Publish as a model
  • Publish code
-

Task Name

It is the name of the task selected on the workbook canvas.

You can click the text field to edit or modify the name of the task as required.

Dependent Variable

It allows you to select the dependent variable.

  • You can select only one variable, and it should be of numeric type.

Independent Variables

It allows you to select independent variables.

  • You can select more than one variable.
  • You can select variables of any type.
  • If categorical or textual variables are selected, you need to use Label Encoders.

Advanced

Alpha

It allows you to enter a constant that multiplies the L1 term.

The default value is 1.0.

Fit Intercept

It allows you to select whether you want to calculate the value of constant (c) for your model.

  • You can select either True or False.
  • Selecting True will calculate the value of the constant.
  • The default value is True.

Maximum Iteration

It allows you to enter the maximum number of iterations.

—

Tolerance

It allows you to enter the precision of the solution.

The default value is 0.001.

Solver

It allows you to select the method to use to compute the Ridge coefficients.

The available methods are:

  • Auto - It selects the solver automatically based on the type of data.
  • Auto is the default value.
  • Svd -  It uses a Singular Value Decomposition of X.
  • Cholesky – It uses the standard scipy.linalg.solve function.
  • Sparse_cg - It uses the conjugate gradient solver.
  • Lsqr - It uses the dedicated regularized least-squares routine. It is the fastest.
  • Sag – It uses Stochastic Average Gradient descent.
  • Saga – It uses an improved, unbiased version of Stochastic Average Gradient descent.

Random State

It allows you to enter the seed of the random number generator.

This value is used only when Selection is set to random, and the Solver method selected is Sag or Saga.

Dimensionality Reduction

It allows you to select the method for dimensionality reduction.

  • The available options are – None and PCA.
  • The default value is None.
Add result as a variableIt allows you to select whether the result of the algorithm is to be added as a variable.
Node ConfigurationIt allows you to select the instance of the AWS server to provide control on the execution of a task in a workbook or workflow.
Hyper Parameter OptimizationIt allows you to select parameters for optimization

Example of Ridge Regression

Consider a dataset of Credit Card balances of people of different gender, age, education, and so on. A snippet of input data is shown in the figure given below.


We select Limit, Balance, Income, Cards, and Age as the independent variables and Rating as the dependent variable. The result of Ridge Regression is displayed in the figure below.


The table below describes the various performance metrics on the result page.

Performance Metric

Description

Remark

RMSE (Root Mean Squared Error)

It is the square root of the averaged squared difference between the actual values and the predicted values.

It is the most commonly used performance metric of the model.

R Square

It is the statistical measure that determines the proportion of variance in the dependent variable that is explained by the independent variables.

Value is always between 0 and 1.

Adjusted R Square

It is an improvement of R Square. It adjusts for the increasing predictors and only shows improvement if there is a real improvement.

Adjusted R Square is always lower than R Square.

AIC (Akaike Information Criterion)

AIC is an estimator of errors in predicted values and signifies the quality of the model for a given dataset.

A model with the least AIC is preferred.

BIC
(Bayesian Information Criterion)

BIC is a criterion for model selection amongst a finite set of models.

A model with the least BIC is preferred.

MSE (Mean Squared Error)It is the averaged squared difference between the actual values and the predicted values.A model with low MSE is preferred.
MAE (Mean Absolute Error)It the absolute value of difference between actual and predicted valuesA model with low MAE is preferred.
MAPE ( Mean Absolute Percentage Error)it is the average magnitude of error produced by a model, or how far off predictions are on average.A model with low MAPE is preferred

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