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Found 8 papers, 4 papers with code
Towards explainable meta-learning
are used to predict the expected performance.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
Interpretable Meta-Measure for Model 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.
Does imputation matter? Benchmark for predictive models
Incomplete data are common in practical applications.
Do not explain without context: addressing the blind spot of model explanations
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.
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
Consolidated learning -- a domain-specific model-free optimization strategy with examples for XGBoost and MIMIC-IV
For many machine learning models, a choice of hyperparameters is a crucial step towards achieving high performance.
SeFNet: Bridging Tabular Datasets with Semantic Feature Nets
In this paper, we present an example of SeFNet prepared for a collection of predictive tasks in healthcare, with the features' relations derived from the SNOMED-CT ontology.
Rethinking of Encoder-based Warm-start Methods in Hyperparameter Optimization
In this work, we evaluate Dataset2Vec and liltab on two common meta-tasks - representing entire datasets and hyperparameter optimization warm-start.
Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making
In many applications, model ensembling proves to be better than a single predictive model.
