Linear Forests generalize the well known Random Forests by combining Linear Models with the same Random Forests. The key idea is to use the strength of Linear ... SciPy and Scikit-Learn (>=0.24.2).

Budoen, A.T., Zhang, M.W. and Edwards Jr., L.Z. (2025) A Comparative Study of Ensemble Learning Techniques and Classification Models to Identify Phishing Websites. Open Access Library Journal, 12, ...

Their manual classification was ... conducted hyperparameter tuning using both manual adjustments and automated optimization. However, tuning did not always yield significant improvements. For ...

The next step involves calculating the residual distribution of TWS at 1° resolution by subtracting the random forest model simulated TWS from the GRACE-derived TWS data. These residuals are then ...

for anti-fraudulent detection. The proposed system is random forest classification algorithm using machine learning algorithms to detect fraudulent activities. The machine learning algorithms designed ...

Our approach addresses a common limitation in manifold alignment, where existing methods often fail to generate embeddings that capture sufficient information for downstream classification. By ...