Shape quantization and recognition with randomized trees
Neural Computation
Machine Learning
Novelty detection: a review—part 1: statistical approaches
Signal Processing
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
A Classification Framework for Anomaly Detection
The Journal of Machine Learning Research
High-quantile modeling for customer wallet estimation and other applications
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Bi-level path following for cross validated solution of kernel quantile regression
Proceedings of the 25th international conference on Machine learning
A New Probabilistic Approach in Rank Regression with Optimal Bayesian Partitioning
The Journal of Machine Learning Research
Consistency of Random Forests and Other Averaging Classifiers
The Journal of Machine Learning Research
Expert Systems with Applications: An International Journal
Bi-Level Path Following for Cross Validated Solution of Kernel Quantile Regression
The Journal of Machine Learning Research
Interval Forecast of Water Quality Parameters
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
2D-interval predictions for time series
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of a random forests model
The Journal of Machine Learning Research
Detecting insider threats in a real corporate database of computer usage activity
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithm runtime prediction: Methods & evaluation
Artificial Intelligence
Model-based boosting in R: a hands-on tutorial using the R package mboost
Computational Statistics
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Random forests were introduced as a machine learning tool in Breiman (2001) and have since proven to be very popular and powerful for high-dimensional regression and classification. For regression, random forests give an accurate approximation of the conditional mean of a response variable. It is shown here that random forests provide information about the full conditional distribution of the response variable, not only about the conditional mean. Conditional quantiles can be inferred with quantile regression forests, a generalisation of random forests. Quantile regression forests give a non-parametric and accurate way of estimating conditional quantiles for high-dimensional predictor variables. The algorithm is shown to be consistent. Numerical examples suggest that the algorithm is competitive in terms of predictive power.