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06_​Logistic_​Regression

Logistic Regression

This workflow is an example of how to build a basic prediction / classification model using logistic regression.

Calculate accuracy andconfusion matrix80% for training set20% for testing setTrain model withSAG and Gausspriorz-normalize the data with0 mean and standarddeviation of 1Read the wine.tabledatasetUse the model to make predictionsNode 323Node 324Scorer (deprecated) Partitioning LogisticRegression Learner Normalizer (PMML) Table Reader(deprecated) Logistic RegressionPredictor Number ToString (PMML) ROC Curve Calculate accuracy andconfusion matrix80% for training set20% for testing setTrain model withSAG and Gausspriorz-normalize the data with0 mean and standarddeviation of 1Read the wine.tabledatasetUse the model to make predictionsNode 323Node 324Scorer (deprecated) Partitioning LogisticRegression Learner Normalizer (PMML) Table Reader(deprecated) Logistic RegressionPredictor Number ToString (PMML) ROC Curve

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: 2017-06-20
On NodePit since: 2022-11-30
Last update: 2024-12-17
Created with KNIME version: v4.6.3
Tags: classification machine learning prediction analytics KNIME logistic regression logit data science

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