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17_​Logistic_​Regression

17_Logistic_Regression
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 Node 1Node 2Node 3Node 4Node 5Node 7 CSV Reader Normalizer (PMML) Partitioning LogisticRegression Learner Logistic RegressionPredictor Scorer (JavaScript) 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 Node 1Node 2Node 3Node 4Node 5Node 7 CSV Reader Normalizer (PMML) Partitioning LogisticRegression Learner Logistic RegressionPredictor Scorer (JavaScript)

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Created by: Maarit Laukkanen
Created at: 2020-04-20
On NodePit since: 2021-03-30
Last update: 2024-05-01
Created with KNIME version: v4.3.2
Tags: e-learninglogistic regressionclassificationeducation

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