Interpretable Machines: Constructing Valid Prediction Intervals with Random Forests
An important issue when using Machine Learning algorithms in recent research is the lack of interpretability. Although these algorithms provide accurate point predictions for various learning problems, uncertainty estimates connected with point predictions are rather sparse. A contribution to this gap for the Random Forest Regression Learner is presented here. Based on its Out-of-Bag procedure, several parametric and non-parametric prediction intervals are provided for Random Forest point predictions and theoretical guarantees for its correct coverage probability is delivered. In a second part, a thorough investigation through Monte-Carlo simulation is conducted evaluating the performance of the proposed methods from three aspects: (i) Analyzing the correct coverage rate of the proposed prediction intervals, (ii) Inspecting interval width and (iii) Verifying the competitiveness of the proposed intervals with existing methods. The simulation yields that the proposed prediction intervals are robust towards non-normal residual distributions and are competitive by providing correct coverage rates and comparably narrow interval lengths, even for comparably small samples.
Code (0)
등록된 구현이 없습니다.
Tasks
PredictionPrediction IntervalsvalidSimilar Papers 제목 키워드 기반
Interpretable Multivariate Conformal Prediction with Fast Transductive Standardization
We propose a conformal prediction method for constructing tight simultaneous prediction intervals for multiple, potentially related, numerical outputs given a single input. This method can be combined with any multi-targ…
Multi-target regressionRelaxed Quantile Regression: Prediction Intervals for Asymmetric Noise
Constructing valid prediction intervals rather than point estimates is a well-established approach for uncertainty quantification in the regression setting. Models equipped with this capacity output an interval of values…
PredictionPrediction Intervalsquantile regressionregression+2Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretability
Conformal Prediction (CP) is a distribution-free framework for constructing statistically rigorous prediction sets. While popular variants such as CD-split improve CP's efficiency, they often yield prediction sets compos…
Distributional conformal prediction
We propose a robust method for constructing conditionally valid prediction intervals based on models for conditional distributions such as quantile and distribution regression. Our approach can be applied to important pr…
Conformal PredictioncounterfactualPredictionPrediction Intervals+4Conformalized Quantile Regression
Conformal prediction is a technique for constructing prediction intervals that attain valid coverage in finite samples, without making distributional assumptions. Despite this appeal, existing conformal methods can be un…
Conformal PredictionPredictionPrediction Intervalsquantile regression+2