Kurniawan, R. (2024) Application of Random Forest Algorithm on Credit Risk Analysis. Procedia Computer Science, ... (SVM), Random Forest, and Multi-Layer Perceptron (MLP). The results show that the ...

Our analysis revealed that Random Forest consistently outperformed other models in balancing predictive accuracy and alignment with financial forecasts. Among the tested configurations, the ...

The model employs the Random Forest Algorithm to provide robust and interpretable predictions. Project Overview. Title: Stroke Prediction Using Random Forest; Course: Machine Learning (IF540-L) ...

Using double bootstrap sampling to create training sets Employing random subspace selection twice for feature selection Combining predictions through weighted voting This approach has been shown to ...

This method is avoided for the lung cancer dataset from Kaggle, and a fragmented model is used to undermine the model's performance. It is seen that among the combinations, the stacked model and SVM ...

In the processing of biphase coded signals, if the ADC sampling moment coincides with the transition edge of the symbol inversion, it can result in the loss of symbol’s peak information. This leads to ...

Building on random forests (RFs) and random intersection trees (RITs) and through extensive, biologically inspired simulations, we developed the iterative random forest algorithm (iRF). iRF trains a ...