By naively using categorical values in rule induction, we risk reducing the chances of finding a good rule in terms both of confidence (accuracy) and of support ...
In this paper we address problems arising from the use of categorical valued data in rule induction. By naively using cat- egorical values in rule induction ...
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By naively using categorical values in rule induction, we risk reducing the chances of finding a good rule in terms both of confidence (accuracy) and of support ...
By naively using categorical values in rule induction, we risk reducing the chances of finding a good rule in terms both of confidence (accuracy) and of support ...
By naively using categorical values in rule induction, we risk reducing the chances of finding a good rule in terms both of confidence (accuracy) and of support ...
Jul 22, 2011 · By naively using categorical values in rule induction, we risk reducing the chances of finding a good rule in terms both of confidence (accuracy) ...
Abstract. We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space.
Mar 19, 2019 · In which cases (algorithms, parameters...) would I want to keep the 1st level and fit with k levels for each categorical variable? EDIT: @ ...
Missing: induction. | Show results with:induction.
Abstract. Rule-based learning algorithms have higher transparency and easiness to interpret in comparison with neural networks and deep learning algorithms.
Jul 12, 2014 · H2O has a very efficient method for handling categorical data directly which often gives it an edge over tree based methods that require one-hot ...
Missing: rule induction.