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04_​Logistic_​Regression_​solution

Logistic Regression - solution

Introduction to Machine Learning Algorithms course - Session 2
Solution to exercise 4
- Train a logistic regression model
- Apply the model to the test set
- Evaluate the model performances with the Scorer node

Exercise: Logistic Regression1) Train a Logistic Regression model to predict the overall condition of a house (high/low) (Logistic Regression Learner node)- Select the "rank" column as the target column- Select "low" as the reference category- Leave other settings to their defaults2) Use the trained model to predict the rank of the houses in the test set (Logistic Regression Predictor node)3) Evaluate the accuracy of the logistic regression model (Scorer (JavaScript) node)- Select "rank" as the actual column and "Prediction (rank)" as the predicted column- What is the accuracy of the model? Classification: Logistic Regression Model EvaluateRead AmesHousing.csv Logistic RegressionPredictor Scorer (JavaScript) CSV Reader LogisticRegression Learner Preprocessing Exercise: Logistic Regression1) Train a Logistic Regression model to predict the overall condition of a house (high/low) (Logistic Regression Learner node)- Select the "rank" column as the target column- Select "low" as the reference category- Leave other settings to their defaults2) Use the trained model to predict the rank of the houses in the test set (Logistic Regression Predictor node)3) Evaluate the accuracy of the logistic regression model (Scorer (JavaScript) node)- Select "rank" as the actual column and "Prediction (rank)" as the predicted column- What is the accuracy of the model? Classification: Logistic Regression Model EvaluateRead AmesHousing.csv Logistic RegressionPredictor Scorer (JavaScript) CSV Reader LogisticRegression Learner Preprocessing

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Created by: Emilio Silvestri
Created at: 2019-11-29
On NodePit since: 2023-04-06
Last update: 2024-04-27
Created with KNIME version: v4.6.1
Tags: classificationlogistic regressioneducation

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