Icon

Logistic_​Regression_​Solution

Logistic Regression

Logistic Regression: predict wine color.

- Normalize numerical columns
- Partition the dataset into train and test set
- Train a Logistic Regression model
- Apply the model to the test set
- Evaluate the model performance

URL: Behind the Scenes of Logistic Regression with KNIME https://youtu.be/-3y_o8j1pBA
URL: Logistic Regression Learner Node: Algorithm Settings https://youtu.be/AclQdjxpGA0
URL: Logistic Regression Learner Node: Output Values and Memory Handling https://youtu.be/ywPvpgFF2i4
URL: Guide to Intelligent Data Science https://www.datascienceguide.org/

Exercise: Logistic Regression In this exercise we will train a Logistic Regresion model that predicts whether a wine is red or white. 1) Read data wine.csv (Hint: drag and drop) 2) Use the Normalizer (PMML) node to z normalize all numerical columns 3) Partition the dataset into a training set (80%) and a test set (20%). - Apply stratified sampling on the color column. 4) Train a logistic regression model on the training set, and apply the model to the test set 5) Use the Scorer node to evaluate the accuracy of the model
Logistic Regresion: color prediction
Train the model to predict wine color
Logistic Regression Learner
Apply the model to the test set
Logistic Regression Predictor
Z-Score Normalization on all numerical columns
Normalizer (PMML)
Top: train set (80%) Bottom: test set (20%) Stratified sampling on color
Partitioning
Evaluate model accuracy
Scorer
Read data wine.csv
CSV Reader

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: Maarit Laukkanen
Created at: 2020-04-20
On NodePit since: 2024-05-18
Last update: 2026-11-10
Created with KNIME version: v5.2.3
Tags: logistic regressionclassificationeducationGIDSexerciseacademiaTheGuideBook

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

Try NodePit Runner!