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Question3

Question3

Question3

Read the wine.csv dataset.
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%) using the Partitioning node
with the stratified sampling option on the column “Income”.
- Use the Logistic Regression Learner Node to train the model on the training set and the Logistic
Regression Predictor Node to apply the model to the test set.
- Use the Scorer node to evaluate the accuracy of the model.

accuracy of the model80% for training set20% for testing settrain modelnormalize data with z - score normalizationmodel tomake predictionsread wine.csvdataset Scorer Partitioning LogisticRegression Learner Normalizer (PMML) Logistic RegressionPredictor File Reader accuracy of the model80% for training set20% for testing settrain modelnormalize data with z - score normalizationmodel tomake predictionsread wine.csvdatasetScorer Partitioning LogisticRegression Learner Normalizer (PMML) Logistic RegressionPredictor File Reader

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Created by: byasar
Created at: 2020-04-11
On NodePit since: 2020-04-19
Last update: 2024-04-25
Created with KNIME version: v4.1.1

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