Conformity in machine learning model prediction evaluation is calculated using a measure called the nonconformity score. This score quantifies how different or "nonconforming" a new data point is compared to the patterns observed in the training data[2]. The process of calculating conformity involves several steps:
1. Training Phase:
- Split the dataset into a proper training set and a calibration set.
- Train the model on the proper training set.
- Use the trained model to make predictions on the calibration set.
2. Nonconformity Calculation:
- For each instance in the calibration set, calculate a nonconformity score.
- This score measures how different the prediction is from the actual value.
3. Prediction Phase:
- For a new data point, calculate its nonconformity score using the trained model.
- Compare this score to the distribution of nonconformity scores from the calibration set.
The nonconformity score can be calculated in various ways, depending on the type of problem:
- For regression: It could be the absolute difference between the predicted and actual values.
- For classification: It might be based on the probability assigned to the correct class.
The key idea is that instances with higher nonconformity scores are less conforming to the training patterns and are therefore associated with higher uncertainty[2].
By using this approach, Inductive Conformal Prediction (ICP) can generate prediction intervals or sets that capture the uncertainty associated with individual predictions. This allows for a more nuanced evaluation of model performance, going beyond simple point predictions to provide a measure of confidence in each prediction.
Citations:
[1] https://www.geeksforgeeks.org/metrics-for-machine-learning-model/
[2] https://www.linkedin.com/pulse/inductive-conformal-prediction-yeshwanth-n
[3] https://kanerika.com/glossary/model-evaluation-metrics/
[4] https://www.youtube.com/watch?v=oqK6rM8fbkk
[5] https://towardsdatascience.com/metrics-to-evaluate-your-machine-learning-algorithm-f10ba6e38234?gi=7cd5f38faaf8
[6] https://www.datasource.ai/en/data-science-articles/model-evaluation-metrics-in-machine-learning
[7] https://www.nature.com/articles/s41598-024-56706-x
[8] https://towardsdatascience.com/all-you-need-is-conformal-prediction-726f18920241?gi=6dd1cfc4136e
Conformity and SHAP (SHapley Additive exPlanations) value analysis are related in the context of machine learning model interpretation and uncertainty quantification. Both approaches aim to provide insights into model behavior, but they focus on different aspects:
1. Uncertainty Quantification: Conformity measures, particularly in the form of nonconformity scores, are used to quantify the uncertainty of model predictions. SHAP values, on the other hand, explain the impact of individual features on model outputs[1][3].
2. Shapley-value Conformity Scores: Recent research has explored combining Shapley values with conformal prediction to create more informative prediction sets. This approach uses Shapley values as conformity scores, resulting in smaller prediction sets for certain significance levels compared to traditional methods[5].
3. Complementary Information: While SHAP values provide feature importance and impact on model predictions, conformity measures offer insights into the reliability and uncertainty of those predictions. Together, they can provide a more comprehensive understanding of model behavior[2].
4. Uncertainty in SHAP Values: Research has also focused on quantifying uncertainty in SHAP value estimations. This includes using Shapley Residuals, Mean-Standard-Error, and Bayesian SHAP to capture different sources of uncertainty in SHAP explanations[6].
5. Application to Uncertainty Explanation: Recent work has adapted the Shapley value framework to explain various types of predictive uncertainty, quantifying each feature's contribution to the conditional entropy of model outputs[4].
By combining conformity measures with SHAP value analysis, researchers and practitioners can gain a more nuanced understanding of both model predictions and their associated uncertainties, leading to more reliable and interpretable machine learning applications.
Citations:
[1] https://proceedings.neurips.cc/paper_files/paper/2023/file/16e4be78e61a3897665fa01504e9f452-Paper-Conference.pdf
[2] https://papers.phmsociety.org/index.php/phmap/article/download/3694/2161
[3] https://mindfulmodeler.substack.com/p/shap-is-not-all-you-need
[4] https://arxiv.org/abs/2306.05724
[5] https://proceedings.mlr.press/v152/jaramillo21a.html
[6] https://scholarship.tricolib.brynmawr.edu/items/1c209352-e4ab-454e-822c-1fe30211b92d
[7] https://pmc.ncbi.nlm.nih.gov/articles/PMC10985608/
[8] https://soil.copernicus.org/articles/10/679/2024/
Conformity and attention masks in transformers can be combined in innovative ways to enhance model performance and uncertainty quantification. Here are some key approaches:
1. Uncertainty-Guided Transformer (UGT): This approach uses conformity measures to guide the attention mechanism. By introducing an uncertainty-guided random masking algorithm (UGRM), higher probability of masking is assigned to uncertain regions during training. This forces the transformer to become more efficient at inferring and recovering content in uncertain regions by exploiting contextual information.[2]
2. Stochastic Attention: Instead of using deterministic attention distributions, the attention mechanism can be made stochastic. This involves sampling attention from a Gumbel-Softmax distribution, which controls the concentration over values. Additionally, key heads in self-attention can be regularized to attend to a set of learnable centroids, effectively performing clustering over keys or hidden states.[4]
3. Probabilistic Transformer: This approach uses probabilistic attention scores to quantify epistemic uncertainties in model predictions. It involves training two models - a majority model focusing on low-uncertainty samples and a minority model focusing on high-uncertainty samples. During inference, these models are dynamically combined based on the input uncertainty to make the final prediction.[6]
4. Transformer Conformal Prediction: This method uses the Transformer architecture, particularly the decoder, as a conditional quantile estimator to predict the quantiles of prediction residuals. These quantiles are then used to estimate prediction intervals. The Transformer's ability to learn temporal dependencies across past prediction residuals benefits the estimation of prediction intervals.[5]
5. Topological Feature Extraction: This approach extracts topological features from attention matrices, providing a low-dimensional, interpretable representation of the model's internal dynamics. This can be used to estimate uncertainty in the transformer's predictions.[8]
These approaches demonstrate how conformity measures and attention masks can be combined to improve uncertainty quantification, enhance model interpretability, and potentially boost performance in various tasks. By integrating these concepts, researchers can develop more robust and reliable transformer models that not only make accurate predictions but also provide valuable insights into their confidence levels.
Citations:
[1] https://www.reddit.com/r/MLQuestions/comments/1fqjdrf/understanding_masked_attention_in_transformer/
[2] https://openaccess.thecvf.com/content/ICCV2021/papers/Yang_Uncertainty-Guided_Transformer_Reasoning_for_Camouflaged_Object_Detection_ICCV_2021_paper.pdf
[3] https://proceedings.mlr.press/v206/seedat23a/seedat23a.pdf
[4] https://cdn.aaai.org/ojs/21364/21364-13-25377-1-2-20220628.pdf
[5] https://arxiv.org/html/2406.05332v1
[6] https://sites.ecse.rpi.edu/~cvrl/Publication/pdf/Guo2022.pdf
[7] https://nejsds.nestat.org/journal/NEJSDS/article/10/text
[8] https://arxiv.org/abs/2308.11295
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