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A study of the GeGDP problem base on the LightGBM regression
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This study uses AHP hierarchy analysis to select three indicators as the measurement standard of green GDP: GDP, resource consumption and reduction cost and environmental degradation cost, and analyzes the weight of green GDP indicators and the importance of the three indicators. With GeGDP as the main indicator of a country's economic health, a Lasso regression model is established to analyze the predicted global climate mitigation impacts. Then, this paper establishes LightGBPGBM regression model to predict the future GeGDP and GDP of the United States, and uses r square and other indicators to test the accuracy of the model, and makes a human correlation analysis of GeGDP and GDP before and after the prediction, to analyze the degree of correlation between the two. Finally, taking the United States as an example, we substituted relevant data into the LightGBM regression model developed above, predicted its value, and conducted one-way analysis of variance on it to determine the degree of change before and after. This study uses AHP hierarchy analysis to select three indicators as the measurement standard of green GDP: GDP, resource consumption and reduction cost and environmental degradation cost, and analyzes the weight of green GDP indicators and the importance of the three indicators. With GeGDP as the main indicator of a country's economic health, a Lasso regression model is established to analyze the predicted global climate mitigation impacts. Then, this paper establishes LightGBPGBM regression model to predict the future GeGDP and GDP of the United States, and uses r square and other indicators to test the accuracy of the model, and makes a human correlation analysis of GeGDP and GDP before and after the prediction, to analyze the degree of correlation between the two. Finally, taking the United States as an example, we substituted relevant data into the LightGBM regression model developed above, predicted its value, and conducted one-way analysis of variance on it to determine the degree of change before and after.
Title: A study of the GeGDP problem base on the LightGBM regression
Description:
This study uses AHP hierarchy analysis to select three indicators as the measurement standard of green GDP: GDP, resource consumption and reduction cost and environmental degradation cost, and analyzes the weight of green GDP indicators and the importance of the three indicators.
With GeGDP as the main indicator of a country's economic health, a Lasso regression model is established to analyze the predicted global climate mitigation impacts.
Then, this paper establishes LightGBPGBM regression model to predict the future GeGDP and GDP of the United States, and uses r square and other indicators to test the accuracy of the model, and makes a human correlation analysis of GeGDP and GDP before and after the prediction, to analyze the degree of correlation between the two.
Finally, taking the United States as an example, we substituted relevant data into the LightGBM regression model developed above, predicted its value, and conducted one-way analysis of variance on it to determine the degree of change before and after.
This study uses AHP hierarchy analysis to select three indicators as the measurement standard of green GDP: GDP, resource consumption and reduction cost and environmental degradation cost, and analyzes the weight of green GDP indicators and the importance of the three indicators.
With GeGDP as the main indicator of a country's economic health, a Lasso regression model is established to analyze the predicted global climate mitigation impacts.
Then, this paper establishes LightGBPGBM regression model to predict the future GeGDP and GDP of the United States, and uses r square and other indicators to test the accuracy of the model, and makes a human correlation analysis of GeGDP and GDP before and after the prediction, to analyze the degree of correlation between the two.
Finally, taking the United States as an example, we substituted relevant data into the LightGBM regression model developed above, predicted its value, and conducted one-way analysis of variance on it to determine the degree of change before and after.
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