when there is a highly correlated predictor with target











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I am building a predictive model to predict products' price. There is a one predictor(say V1) that is extremely correlated with the price(target) variable(correlation coefficient of 0.991). The problem is when I directly make a rf model to predict price, the rmse is relatively high. But when I make a rf model predicting (price/V1) and then multiply V1 variable to the predictions, the rmse get very low. Why does this happen? And what should I do to improve this model?






correlation data-science prediction







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  • please add some sample data ...and results which u tried
    – sai saran
    Nov 9 at 5:37















up vote
-2
down vote

favorite












I am building a predictive model to predict products' price. There is a one predictor(say V1) that is extremely correlated with the price(target) variable(correlation coefficient of 0.991). The problem is when I directly make a rf model to predict price, the rmse is relatively high. But when I make a rf model predicting (price/V1) and then multiply V1 variable to the predictions, the rmse get very low. Why does this happen? And what should I do to improve this model?






correlation data-science prediction







share|improve this question
























  • please add some sample data ...and results which u tried
    – sai saran
    Nov 9 at 5:37













up vote
-2
down vote

favorite









up vote
-2
down vote

favorite











I am building a predictive model to predict products' price. There is a one predictor(say V1) that is extremely correlated with the price(target) variable(correlation coefficient of 0.991). The problem is when I directly make a rf model to predict price, the rmse is relatively high. But when I make a rf model predicting (price/V1) and then multiply V1 variable to the predictions, the rmse get very low. Why does this happen? And what should I do to improve this model?






correlation data-science prediction







share|improve this question















I am building a predictive model to predict products' price. There is a one predictor(say V1) that is extremely correlated with the price(target) variable(correlation coefficient of 0.991). The problem is when I directly make a rf model to predict price, the rmse is relatively high. But when I make a rf model predicting (price/V1) and then multiply V1 variable to the predictions, the rmse get very low. Why does this happen? And what should I do to improve this model?






correlation data-science prediction




correlation data-science prediction






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 9 at 12:38









Andreas

1,7611618




1,7611618










asked Nov 9 at 4:28









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  • please add some sample data ...and results which u tried
    – sai saran
    Nov 9 at 5:37


















  • please add some sample data ...and results which u tried
    – sai saran
    Nov 9 at 5:37
















please add some sample data ...and results which u tried
– sai saran
Nov 9 at 5:37




please add some sample data ...and results which u tried
– sai saran
Nov 9 at 5:37

















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