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JKISeason2-9

Description: In this challenge, your goal is to see which features are the most important in predicting the quality of wine. After doing this analysis, you should create a visualization that shows the features' importances in order.

Solution Summary: I solve this challenge by comparing the root mean squared error obtained with an AutoML regression model over a subset of the data, and over the whole data with feature value permutations -- one feature at a time. In the end, our conclusion is that alcohol content is likely the most important feature that determines wine quality.

Node 1Node 2Node 3Node 4Node 5Node 6execute up-streambefore configurationNode 8Node 9Node 13Node 14Node 15Node 16Node 17Node 18Node 19Node 20Node 21Node 22Node 23Node 24Node 25Node 26Node 27CSV Reader Row Splitter Partitioning Transpose RowID Row Filter AutoML (Regression) Workflow Executor Numeric Scorer Row Filter Table Rowto Variable Table Row ToVariable Loop Start Counting Loop Start Target Shuffling Row Filter Numeric Scorer Workflow Executor Column Expressions Loop End GroupBy Loop End Numeric Outliers Numeric Outliers(Apply) Bar Chart (Labs) Node 1Node 2Node 3Node 4Node 5Node 6execute up-streambefore configurationNode 8Node 9Node 13Node 14Node 15Node 16Node 17Node 18Node 19Node 20Node 21Node 22Node 23Node 24Node 25Node 26Node 27CSV Reader Row Splitter Partitioning Transpose RowID Row Filter AutoML (Regression) Workflow Executor Numeric Scorer Row Filter Table Rowto Variable Table Row ToVariable Loop Start Counting Loop Start Target Shuffling Row Filter Numeric Scorer Workflow Executor Column Expressions Loop End GroupBy Loop End Numeric Outliers Numeric Outliers(Apply) Bar Chart (Labs)

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Created by: elimi
Created at: 2023-05-24
On NodePit since: 2023-07-22
Last update: 2026-01-22
Created with KNIME version: v4.7.4
Tags: JKISeason2-9

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