A Random Forest Regression Model for Predicting Residual Stresses and Cutting Forces Introduced by Turning IN718 Alloy

Dong, Penghao; Peng, Huachen; Cheng, Xianqiang; Xing, Yan; Zhou, Xin; Huang, Dongliang

doi:10.1109/iccce48422.2019.9010767

Search citation statements

Order By: Relevance

Paper Sections

Select...

Random Forest Regressor1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2023

Publication Types

Select...

Other3

Article2

Book1

Relationship

Self Cite0

Independent6

Authors

Journals

Cited by 8 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Random Forest technique can reflect the interaction between factors and makes it simple to calculate the nonlinear effects of variables. The nonlinear relationships between the dependent and independent variables can therefore be expressed [8].…”

Section: Random Forest Regressormentioning

confidence: 99%

Application of Machine Learning in Boston House Price Prediction

Zhang

2023

AEMPS

View full text Add to dashboard Cite

House prices are an important economic indicator for a country or region, rising house prices are often associated with economic growth and increased employment opportunities, while a decline in house prices may indicate an economic slowdown or other unfavorable factors. Governments and relevant agencies need to understand the dynamics of housing prices to formulate appropriate housing policies and plan urban development. As one of the major economic centers in the United States, the fluctuations in Boston's housing prices can reflect the local economic conditions and development trends. This paper selects data from Boston in 1970. Each record in this database describes a Boston suburb or town. The proposed method was evaluated using 5 metrics. By comparison, the paper ends up with the result that XGBoost works best out of the four regression models.

show abstract

Section: Random Forest Regressormentioning

confidence: 99%