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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

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Created by: Maarit Laukkanen
Created at: 2020-04-20
On NodePit since: 2020-08-06
Last update: 2024-04-25
Created with KNIME version: v4.1.2
Tags: e-learninglogistic regressionclassificationeducation

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