Search Results for author: Etienne van de Bijl

Found 2 papers, 1 papers with code

The Optimal Input-Independent Baseline for Binary Classification: The Dutch Draw

no code implementations9 Jan 2023 Joris Pries, Etienne van de Bijl, Jan Klein, Sandjai Bhulai, Rob van der Mei

The goal of this paper is to examine all baseline methods that are independent of feature values and determine which model is the `best' and why.

Binary Classification

The Dutch Draw: Constructing a Universal Baseline for Binary Prediction Models

1 code implementation24 Mar 2022 Etienne van de Bijl, Jan Klein, Joris Pries, Sandjai Bhulai, Mark Hoogendoorn, Rob van der Mei

Summarizing, the DD baseline is: (1) general, as it is applicable to all binary classification problems; (2) simple, as it is quickly determined without training or parameter-tuning; (3) informative, as insightful conclusions can be drawn from the results.

Binary Classification

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