Double Random Forest is an ensemble learning method that improves upon traditional random forests by: Using double bootstrap sampling to create training sets Employing random subspace selection twice ...

Startups are an important element in innovation and economic growth. However, startup failure is very high, so investors, governments, and startups need to predict startup success. This research ...

Then, using the Semi-automatic parameter adjustment method to adjust the parameters of Random Forest, XGBoost and Gradient-enhanced algorithms to find the optimal parameters. It is found that the ...

For applications in classification problems, Random Forest algorithm will avoid the overfitting problem For both classification and regression task, the same random forest algorithm can be used The ...

Random Forest is a supervised machine learning algorithm which is based on ensemble learning. In this project, I build two Random Forest Classifier models to predict the safety of the car, one with 10 ...