A performance characteristic curve for model evaluation: the application in information diffusion prediction

• URL: http://arxiv.org/abs/2309.09537v2
• Date: Tue, 19 Sep 2023 13:13:29 GMT
• Title: A performance characteristic curve for model evaluation: the application in information diffusion prediction
• Authors: Wenjin Xie, Xiaomeng Wang, Rados{\l}aw Michalski, Tao Jia
• Abstract summary: We propose a metric based on information entropy to quantify the randomness in diffusion data, then identify a scaling pattern between the randomness and the prediction accuracy of the model. Data points in the patterns by different sequence lengths, system sizes, and randomness all collapse into a single curve, capturing a model's inherent capability of making correct predictions. The validity of the curve is tested by three prediction models in the same family, reaching conclusions in line with existing studies.
• Score: 3.8711489380602804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The information diffusion prediction on social networks aims to predict future recipients of a message, with practical applications in marketing and social media. While different prediction models all claim to perform well, general frameworks for performance evaluation remain limited. Here, we aim to identify a performance characteristic curve for a model, which captures its performance on tasks of different complexity. We propose a metric based on information entropy to quantify the randomness in diffusion data, then identify a scaling pattern between the randomness and the prediction accuracy of the model. Data points in the patterns by different sequence lengths, system sizes, and randomness all collapse into a single curve, capturing a model's inherent capability of making correct predictions against increased uncertainty. Given that this curve has such important properties that it can be used to evaluate the model, we define it as the performance characteristic curve of the model. The validity of the curve is tested by three prediction models in the same family, reaching conclusions in line with existing studies. Also, the curve is successfully applied to evaluate two distinct models from the literature. Our work reveals a pattern underlying the data randomness and prediction accuracy. The performance characteristic curve provides a new way to systematically evaluate models' performance, and sheds light on future studies on other frameworks for model evaluation.

Related papers

• Influence Functions for Scalable Data Attribution in Diffusion Models [52.92223039302037]
  Diffusion models have led to significant advancements in generative modelling. Yet their widespread adoption poses challenges regarding data attribution and interpretability. In this paper, we aim to help address such challenges by developing an textitinfluence functions framework.
  arXiv  Detail & Related papers  (2024-10-17T17:59:02Z)
• Deep Non-Parametric Time Series Forecaster [19.800783133682955]
  The proposed approach does not assume any parametric form for the predictive distribution and instead generates predictions by sampling from the empirical distribution according to a tunable strategy. We develop a global version of the proposed method that automatically learns the sampling strategy by exploiting the information across multiple related time series.
  arXiv  Detail & Related papers  (2023-12-22T12:46:30Z)
• A prediction and behavioural analysis of machine learning methods for modelling travel mode choice [0.26249027950824505]
  We conduct a systematic comparison of different modelling approaches, across multiple modelling problems, in terms of the key factors likely to affect model choice. Results indicate that the models with the highest disaggregate predictive performance provide poorer estimates of behavioural indicators and aggregate mode shares. It is also observed that the MNL model performs robustly in a variety of situations, though ML techniques can improve the estimates of behavioural indices such as Willingness to Pay.
  arXiv  Detail & Related papers  (2023-01-11T11:10:32Z)
• Stability of clinical prediction models developed using statistical or machine learning methods [0.5482532589225552]
  Clinical prediction models estimate an individual's risk of a particular health outcome, conditional on their values of multiple predictors. Many models are developed using small datasets that lead to instability in the model and its predictions (estimated risks) We show instability in a model's estimated risks is often considerable, and manifests itself as miscalibration of predictions in new data.
  arXiv  Detail & Related papers  (2022-11-02T11:55:28Z)
• Pathologies of Pre-trained Language Models in Few-shot Fine-tuning [50.3686606679048]
  We show that pre-trained language models with few examples show strong prediction bias across labels. Although few-shot fine-tuning can mitigate the prediction bias, our analysis shows models gain performance improvement by capturing non-task-related features. These observations alert that pursuing model performance with fewer examples may incur pathological prediction behavior.
  arXiv  Detail & Related papers  (2022-04-17T15:55:18Z)
• Conformal prediction for the design problem [72.14982816083297]
  In many real-world deployments of machine learning, we use a prediction algorithm to choose what data to test next. In such settings, there is a distinct type of distribution shift between the training and test data. We introduce a method to quantify predictive uncertainty in such settings.
  arXiv  Detail & Related papers  (2022-02-08T02:59:12Z)
• Dense Uncertainty Estimation [62.23555922631451]
  In this paper, we investigate neural networks and uncertainty estimation techniques to achieve both accurate deterministic prediction and reliable uncertainty estimation. We work on two types of uncertainty estimations solutions, namely ensemble based methods and generative model based methods, and explain their pros and cons while using them in fully/semi/weakly-supervised framework.
  arXiv  Detail & Related papers  (2021-10-13T01:23:48Z)
• Characterizing Fairness Over the Set of Good Models Under Selective Labels [69.64662540443162]
  We develop a framework for characterizing predictive fairness properties over the set of models that deliver similar overall performance. We provide tractable algorithms to compute the range of attainable group-level predictive disparities. We extend our framework to address the empirically relevant challenge of selectively labelled data.
  arXiv  Detail & Related papers  (2021-01-02T02:11:37Z)
• Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning [109.74041512359476]
  We study a number of design decisions for the predictive model in visual MBRL algorithms. We find that a range of design decisions that are often considered crucial, such as the use of latent spaces, have little effect on task performance. We show how this phenomenon is related to exploration and how some of the lower-scoring models on standard benchmarks will perform the same as the best-performing models when trained on the same training data.
  arXiv  Detail & Related papers  (2020-12-08T18:03:21Z)
• A Causal Lens for Peeking into Black Box Predictive Models: Predictive Model Interpretation via Causal Attribution [3.3758186776249928]
  We aim to address this problem in settings where the predictive model is a black box. We reduce the problem of interpreting a black box predictive model to that of estimating the causal effects of each of the model inputs on the model output. We show how the resulting causal attribution of responsibility for model output to the different model inputs can be used to interpret the predictive model and to explain its predictions.
  arXiv  Detail & Related papers  (2020-08-01T23:20:57Z)
• A comprehensive study on the prediction reliability of graph neural networks for virtual screening [0.0]
  We investigate the effects of model architectures, regularization methods, and loss functions on the prediction performance and reliability of classification results. Our result highlights that correct choice of regularization and inference methods is evidently important to achieve high success rate.
  arXiv  Detail & Related papers  (2020-03-17T10:13:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.