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05_​Feature_​Selection_​solution

Feature Selection - solution
Exercise: Feature SelectionLet's take a look at which features are the most important in predicting the rank of a house. Below you see the workflow that we builtearlier in the course.1) Apply a forward feature selection algorithm to the trained model (Forward Feature Selection metanode)- Replace the Learner and Predictor nodes by the Logistic Regression Learner and Logistic Regression Predictor nodes in the workflow- Exclude the "rank" column in the Feature Selection Loop Start node- Select stratified sampling on the "rank" column in the Partitioning node- Select Cohen's kappa as the metric to maximize in the Feature Selection Loop End node- Take a look at the Feature Selection Filter node. Which columns can you leave out and still obtain the maximum Cohen's kappa? 2) Use the Reference Column Filter node to apply forward feature selection to the test set Start feature selection loop here Read AmesHousing.csvapply to the test set CSV Reader Missing ValueHandling Outlier Detection Preprocessing DimensionalityReduction Forward FeatureSelection ReferenceColumn Filter Exercise: Feature SelectionLet's take a look at which features are the most important in predicting the rank of a house. Below you see the workflow that we builtearlier in the course.1) Apply a forward feature selection algorithm to the trained model (Forward Feature Selection metanode)- Replace the Learner and Predictor nodes by the Logistic Regression Learner and Logistic Regression Predictor nodes in the workflow- Exclude the "rank" column in the Feature Selection Loop Start node- Select stratified sampling on the "rank" column in the Partitioning node- Select Cohen's kappa as the metric to maximize in the Feature Selection Loop End node- Take a look at the Feature Selection Filter node. Which columns can you leave out and still obtain the maximum Cohen's kappa? 2) Use the Reference Column Filter node to apply forward feature selection to the test set Start feature selection loop here Read AmesHousing.csvapply to the test set CSV Reader Missing ValueHandling Outlier Detection Preprocessing DimensionalityReduction Forward FeatureSelection ReferenceColumn Filter

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Created by: Emilio Silvestri
Created at: 2019-11-29
On NodePit since: 2022-11-15
Last update: 2024-04-28
Created with KNIME version: v4.6.1
Tags: forward feature selectionclassificationlogistic regressioneducation

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