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Linear regression with autofeat engineered features-II

Linear Regression on Ocenographic Data with 'autofeat' Engineered Features
Using existing features Using existing+generated features Using Autofeat apply Using autofeat generated featuresAbout DatasetCalCOFIOver 60 years of oceanographic dataKaggle Refernce: https://www.kaggle.com/sohier/calcofiAbout Problem and ResultsProblem: Building a model to predict Ocean Tempetaure given certain parameters.Result 1: Linear Regression model gives an R-square of 0.778Result 2: Linear Regression model with autofeat generated features gives and R-square of 0.995 Features selected:.1. Depthm2. T_degC : Target3. Salnty4. STheta 80:20812174 X 4Node 44r-sequare: 0.995r-square: 0.778864863 X 4Node 59Droprows withmissing values864863 X 74249MB Partitioning Normalizer Linear RegressionLearner RegressionPredictor Numeric Scorer Linear RegressionLearner RegressionPredictor Numeric Scorer Column Filter Statistics Missing Value Table Reader Autofeat Generator Autofeat Apply Using existing features Using existing+generated features Using Autofeat apply Using autofeat generated featuresAbout DatasetCalCOFIOver 60 years of oceanographic dataKaggle Refernce: https://www.kaggle.com/sohier/calcofiAbout Problem and ResultsProblem: Building a model to predict Ocean Tempetaure given certain parameters.Result 1: Linear Regression model gives an R-square of 0.778Result 2: Linear Regression model with autofeat generated features gives and R-square of 0.995 Features selected:.1. Depthm2. T_degC : Target3. Salnty4. STheta 80:20812174 X 4Node 44r-sequare: 0.995r-square: 0.778864863 X 4Node 59Droprows withmissing values864863 X 74249MB Partitioning Normalizer Linear RegressionLearner RegressionPredictor Numeric Scorer Linear RegressionLearner RegressionPredictor Numeric Scorer Column Filter Statistics Missing Value Table Reader Autofeat Generator Autofeat Apply

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