Icon

17_​Logistic_​Regression - Solution

17_Logistic_Regression - Solution
Exercise Logistic Regression1) Read data wine.csv2) Train a Logistic Regression Model to predict whether a wine is red or white- Use the Normalizer (PMML) node to z normalize all numerical columns- Partition the dataset into a training set (80%) and a test set (20%). Apply stratified sampling on the color column.- Train a logistic regression model on the training set, and apply the model to the test set- OPTIONAL: use the Scorer node to evaluate the accuracy of the model Read datawine.csvTrain the modelto predict wine colorApply the modelto the test setZ-Score Normalizationon all numerical columnsTop: train set (80%)Bottom: test set (20%)Stratified sampling on colorEvaluate model accuracy File Reader LogisticRegression Learner Logistic RegressionPredictor Normalizer (PMML) Partitioning Scorer Exercise Logistic Regression1) Read data wine.csv2) Train a Logistic Regression Model to predict whether a wine is red or white- Use the Normalizer (PMML) node to z normalize all numerical columns- Partition the dataset into a training set (80%) and a test set (20%). Apply stratified sampling on the color column.- Train a logistic regression model on the training set, and apply the model to the test set- OPTIONAL: use the Scorer node to evaluate the accuracy of the model Read datawine.csvTrain the modelto predict wine colorApply the modelto the test setZ-Score Normalizationon all numerical columnsTop: train set (80%)Bottom: test set (20%)Stratified sampling on colorEvaluate model accuracyFile Reader LogisticRegression Learner Logistic RegressionPredictor Normalizer (PMML) Partitioning Scorer

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: 2021-04-15
Last update: 2024-11-21
Created with KNIME version: v4.1.2
Tags: e-learninglogistic regressionclassificationeducation

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

Try NodePit Runner!