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Linear regression with autofeat engineered features-I

Linear Regression with 'autofeat' Engineered Features
Using existing features Using existing+generated features Using autofeat feature predictor About the problemThe problem pertains to predicting concrete compressive strength from variousfeatures such as: Cement, Blast Furnace Slag, Fly Ash, Water, Superplasticizer,Coarse Aggregate, Fine Aggregate and Age. Linear regression using generatedfeatures gives better results than without it. Concrete Compressive Strength Data Set with full description of features is availableat UCI repo. 80:20normalize allfeatures buttargetNode 441030 X9Node 60 Partitioning Normalizer Linear RegressionLearner RegressionPredictor Numeric Scorer Linear RegressionLearner RegressionPredictor Numeric Scorer Table Reader Autofeat Apply Autofeat Generator Table Writer Using existing features Using existing+generated features Using autofeat feature predictor About the problemThe problem pertains to predicting concrete compressive strength from variousfeatures such as: Cement, Blast Furnace Slag, Fly Ash, Water, Superplasticizer,Coarse Aggregate, Fine Aggregate and Age. Linear regression using generatedfeatures gives better results than without it. Concrete Compressive Strength Data Set with full description of features is availableat UCI repo. 80:20normalize allfeatures buttargetNode 441030 X9Node 60Partitioning Normalizer Linear RegressionLearner RegressionPredictor Numeric Scorer Linear RegressionLearner RegressionPredictor Numeric Scorer Table Reader Autofeat Apply Autofeat Generator Table Writer

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Created by: ashok
Created at: 2021-09-05
On NodePit since: 2021-09-11
Last update: 2024-04-30
Created with KNIME version: v4.4.1
Tags: Explainable modellinear regressioncement strength

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