What are the KNIME nodes needed for the training/test portion of this model, in order of sequence: Excel Reader Node, Partitioning Node, Logistic Regression Learner Node, Logistic Regression Predictor Node, Scorer Node, and the Lift Chart Node. For the prediction portion of the model, the following nodes are needed, in order of sequence: Excel Reader Node and the Logistic Regression Predictor Node. * Just need help with what the flow diagram looks like* Excel Reader Node (for the training/test portion of the model): Partitioning Node: Specify 80% for the Training partition size. Check the "Use Random Seed" box. Logistic Learner Node Use the Logistic Regression Predictor Node to validate the Test data. Use the Scorer Node and the Lift Chart Node to view the Test data model performance results.

What are the KNIME nodes needed for the training/test portion of this model, in order of sequence: Excel Reader Node, Partitioning Node, Logistic Regression Learner Node, Logistic Regression Predictor Node, Scorer Node, and the Lift Chart Node. For the prediction portion of the model, the following nodes are needed, in order of sequence: Excel Reader Node and the Logistic Regression Predictor Node. * Just need help with what the flow diagram looks like* Excel Reader Node (for the training/test portion of the model): Partitioning Node: Specify 80% for the Training partition size. Check the "Use Random Seed" box. Logistic Learner Node Use the Logistic Regression Predictor Node to validate the Test data. Use the Scorer Node and the Lift Chart Node to view the Test data model performance results.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Similar questions

The hyper-parameters of a model must NOT be tuned on the test data ( i.e, the data used to evaluate the performance of the final model after selecting the hyper-parameters) Group of answer choices True False
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A model can only be evaluated based on its performance on test data. explain in detail, expand on? Explain?
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5 of 5 You are evaluating a classification model and notice that the error on the test set is significantly greater than on the training set. You determine that the model is overfitting the training data. What could you do to prevent overfitting? Reduce the number of features to model Increase the train/test split ratio Decrease the train/test split ratio Use a different error function NEXT
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