Actual vs Predicted value plots for a) Support Vector Regression, b) Decision Tree Regression, c) Random Forest Regression, d) XGBoost Regression, e) CatBoost Regression, f) AdaBoost Regression, g) Extra Tree Regression and h) Gradient Boosting Regression algorithms.

<p>Actual vs Predicted value plots for a) Support Vector Regression, b) Decision Tree Regression, c) Random Forest Regression, d) XGBoost Regression, e) CatBoost Regression, f) AdaBoost Regression, g) Extra Tree Regression and h) Gradient Boosting Regression algorithms.</p>

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Bibliographic Details
Main Author:	Akshansh Mishra (21001499) (author)
Other Authors:	Vijaykumar Jatt (21001502) (author), Eyob Messele Sefene (21001505) (author), Sachin Salunkhe (18385161) (author), Robert Cep (18385164) (author), Emad Abouel Nasr (8960795) (author)
Published:	2025
Subjects:	Biophysics Medicine Genetics Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified peak temperature distribution integrated ml approach informed neural networks eight regression algorithms classify deposition quality supervised machine learning hinders process optimization afsd still need driven learning work employs work demonstrates wave propagation thermal profiles thermal management tailoring microstructure resulting microstructure quantum mechanics process parameters process factors novel solid lowest mse heat transfer fusing data circumvents issues bridging statistical better understood based modeling also applied
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Actual vs Predicted value plots for a) Support Vector Regression, b) Decision Tree Regression, c) Random Forest Regression, d) XGBoost Regression, e) CatBoost Regression, f) AdaBoost Regression, g) Extra Tree Regression and h) Gradient Boosting Regression algorithms.

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