19,417 research outputs found
Non-Parametric Calibration of Probabilistic Regression
The task of calibration is to retrospectively adjust the outputs from a
machine learning model to provide better probability estimates on the target
variable. While calibration has been investigated thoroughly in classification,
it has not yet been well-established for regression tasks. This paper considers
the problem of calibrating a probabilistic regression model to improve the
estimated probability densities over the real-valued targets. We propose to
calibrate a regression model through the cumulative probability density, which
can be derived from calibrating a multi-class classifier. We provide three
non-parametric approaches to solve the problem, two of which provide empirical
estimates and the third providing smooth density estimates. The proposed
approaches are experimentally evaluated to show their ability to improve the
performance of regression models on the predictive likelihood
Binary Classifier Calibration using an Ensemble of Near Isotonic Regression Models
Learning accurate probabilistic models from data is crucial in many practical
tasks in data mining. In this paper we present a new non-parametric calibration
method called \textit{ensemble of near isotonic regression} (ENIR). The method
can be considered as an extension of BBQ, a recently proposed calibration
method, as well as the commonly used calibration method based on isotonic
regression. ENIR is designed to address the key limitation of isotonic
regression which is the monotonicity assumption of the predictions. Similar to
BBQ, the method post-processes the output of a binary classifier to obtain
calibrated probabilities. Thus it can be combined with many existing
classification models. We demonstrate the performance of ENIR on synthetic and
real datasets for the commonly used binary classification models. Experimental
results show that the method outperforms several common binary classifier
calibration methods. In particular on the real data, ENIR commonly performs
statistically significantly better than the other methods, and never worse. It
is able to improve the calibration power of classifiers, while retaining their
discrimination power. The method is also computationally tractable for large
scale datasets, as it is time, where is the number of
samples
Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions
It is often observed that the probabilistic predictions given by a machine
learning model can disagree with averaged actual outcomes on specific subsets
of data, which is also known as the issue of miscalibration. It is responsible
for the unreliability of practical machine learning systems. For example, in
online advertising, an ad can receive a click-through rate prediction of 0.1
over some population of users where its actual click rate is 0.15. In such
cases, the probabilistic predictions have to be fixed before the system can be
deployed.
In this paper, we first introduce a new evaluation metric named field-level
calibration error that measures the bias in predictions over the sensitive
input field that the decision-maker concerns. We show that existing post-hoc
calibration methods have limited improvements in the new field-level metric and
other non-calibration metrics such as the AUC score. To this end, we propose
Neural Calibration, a simple yet powerful post-hoc calibration method that
learns to calibrate by making full use of the field-aware information over the
validation set. We present extensive experiments on five large-scale datasets.
The results showed that Neural Calibration significantly improves against
uncalibrated predictions in common metrics such as the negative log-likelihood,
Brier score and AUC, as well as the proposed field-level calibration error.Comment: WWW 202
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