Icon

08_​Regularized_​Logistic_​Regression

Impact of Regularization in case of Logistic Regression
Data Reading and Preprocessing Generation of Training and Test Set Model Training with different Regularization Settings Without Regularization Laplace Regularization Gauss Regularization Coefficients and Performance Evaluation Impact of Regularization in case of Logistic RegressionThe goal of this workflow is to analyze the impact of different priors in case of the logistic regression. The workflow therefore first reads the internet advertisement dataset. Then it creates a subset with more columns than rows, favouring overfitting. In the next step three models with different prior options are trained. In the last stepthe results are summarized in an interactive javascript view. LaplaceGaussUniformAll inputfeatures for fasterconvergence of the algorithmWith more columns than rows LogisticRegression Learner LogisticRegression Learner Logistic RegressionPredictor Logistic RegressionPredictor Logistic RegressionPredictor LogisticRegression Learner Read andPreprocess Data Normalizer Join Coefficients Join Predictions Delete ConstantColumns Calculate andExtract Accuracy Visualize Results Create Trainingand Test Set Data Reading and Preprocessing Generation of Training and Test Set Model Training with different Regularization Settings Without Regularization Laplace Regularization Gauss Regularization Coefficients and Performance Evaluation Impact of Regularization in case of Logistic RegressionThe goal of this workflow is to analyze the impact of different priors in case of the logistic regression. The workflow therefore first reads the internet advertisement dataset. Then it creates a subset with more columns than rows, favouring overfitting. In the next step three models with different prior options are trained. In the last stepthe results are summarized in an interactive javascript view. LaplaceGaussUniformAll inputfeatures for fasterconvergence of the algorithmWith more columns than rows LogisticRegression Learner LogisticRegression Learner Logistic RegressionPredictor Logistic RegressionPredictor Logistic RegressionPredictor LogisticRegression Learner Read andPreprocess Data Normalizer Join Coefficients Join Predictions Delete ConstantColumns Calculate andExtract Accuracy Visualize Results Create Trainingand Test Set

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: kathrinmelcher
Created at: 2017-11-02
On NodePit since: 2019-10-03
Last update: 2024-04-18
Created with KNIME version: v4.0.1
Tags: regularization gauss laplace L1 L2 logistic regression data science machine learning

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!