Search Results for author:
Found 7 papers, 4 papers with code
Estimating regression errors without ground truth values
Regression analysis is a standard supervised machine learning method used to model an outcome variable in terms of a set of predictor variables.
Guided Visual Exploration of Relations in Data Sets
This paper proposes a principled framework for interactive visual exploration of relations in data, through views most informative given the user's current knowledge and objectives.
Human-guided data exploration using randomisation
An explorative data analysis system should be aware of what the user already knows and what the user wants to know of the data: otherwise the system cannot provide the user with the most informative and useful views of the data.
Human-Guided Data Exploration
This framework allows the user to incorporate existing knowledge into the exploration process, focus on exploring a subset of the data, and compare different complex hypotheses concerning relations in the data.
Interpreting Classifiers through Attribute Interactions in Datasets
In this work we present the novel ASTRID method for investigating which attribute interactions classifiers exploit when making predictions.
Clustering with Confidence: Finding Clusters with Statistical Guarantees
In this study, we propose a technique for quantifying the instability of a clustering solution and for finding robust clusters, termed core clusters, which correspond to clusters where the co-occurrence probability of each data item within a cluster is at least $1 - \alpha$.
Finding Statistically Significant Attribute Interactions
In many data exploration tasks it is meaningful to identify groups of attribute interactions that are specific to a variable of interest.
