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.
This is a companion discussion topic for the original entry at https://kni.me/w/vFAdjUhsJEiQn2Rx