bikes

<p>This data set contains information on daily bike sharing usage in Washington, D.C. for the years 2011-2012. The target variable is <code>count</code>, the total number of bikes lent out to users at a specific day.</p>

datasets

Create local, regional, and global explanations for any machine learning model with forward marginal effects. You provide a model and data, and 'fmeffects' computes feature effects. The package is based on the theory in: C. A. Scholbeck, G. Casalicchio, C. Molnar, B. Bischl, and C. Heumann (2022) <doi:10.48550/arXiv.2201.08837>.

Holger Löwe

fmeffects

Model-Agnostic Interpretations with Forward Marginal Effects

Christian Scholbeck

Christian Heumann

Bernd Bischl

Giuseppe Casalicchio

bikes function

<p>An object of class <code>data.frame</code> with 731 rows and 10 columns.</p>

Format

Regression data of the usage of rental bikes in Washington D.C., USA — bikes

Regression data of the usage of rental bikes in Washington D.C., USA

bikes: Regression data of the usage of rental bikes in Washington D.C., USA

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