Experiments on synthetic and medical data confirm that SurvSHAP(t) can detect variables with a time-dependent effect, and its aggregation is a better determinant of the importance of variables for a prediction than SurvLIME.
To measure the probability of a shot being a goal by the expected goal, several features are used to train an expected goal model which is based on the event and tracking football data.
For many machine learning models, a choice of hyperparameters is a crucial step towards achieving high performance.
LIMEcraft enhances the process of explanation by allowing a user to interactively select semantically consistent areas and thoroughly examine the prediction for the image instance in case of many image features.
The growing number of AI applications, also for high-stake decisions, increases the interest in Explainable and Interpretable Machine Learning (XI-ML).
The increasing number of regulations and expectations of predictive machine learning models, such as so called right to explanation, has led to a large number of methods promising greater interpretability.
The relevance of the Key Information Extraction (KIE) task is increasingly important in natural language processing problems.
What is even more important and valuable we also show how to boost advanced models using techniques which allow to interpret them and made them more accessible for credit risk practitioners, resolving the crucial obstacle in widespread deployment of more complex, 'black box' models like random forests, gradient boosted or extreme gradient boosted trees.
The package includes a series of methods for bias mitigation that aim to diminish the discrimination in the model.
The sudden outbreak and uncontrolled spread of COVID-19 disease is one of the most important global problems today.
Data collected in this way is used to study the factors influencing the algorithm's performance.
For example, the difference in performance for two models has no probabilistic interpretation, there is no reference point to indicate whether they represent a significant improvement, and it makes no sense to compare such differences between data sets.
State-of-the-art solutions for Natural Language Processing (NLP) are able to capture a broad range of contexts, like the sentence-level context or document-level context for short documents.
To our surprise, their development is driven by model developers rather than a study of needs for human end users.
Is it true that patients with similar conditions get similar diagnoses?