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Found 4 papers, 0 papers with code
Explainable Unsupervised Anomaly Detection with Random Forest
We describe the use of an unsupervised Random Forest for similarity learning and improved unsupervised anomaly detection.
Supervised Similarity for High-Yield Corporate Bonds with Quantum Cognition Machine Learning
We investigate the application of quantum cognition machine learning (QCML), a novel paradigm for both supervised and unsupervised learning tasks rooted in the mathematical formalism of quantum theory, to distance metric learning in corporate bond markets.
Quantile Regression using Random Forest Proximities
Recently, quantile regression forests (QRF) have emerged as a promising solution: Unlike most basic quantile regression methods that need separate models for each quantile, quantile regression forests estimate the entire conditional distribution of the target variable with a single model, while retaining all the salient features of a typical random forest.
Enhanced Local Explainability and Trust Scores with Random Forest Proximities
We initiate a novel approach to explain the predictions and out of sample performance of random forest (RF) regression and classification models by exploiting the fact that any RF can be mathematically formulated as an adaptive weighted K nearest-neighbors model.
