Binary Split Categorical feature with Mean Absolute Error Criteria in CART
In the context of the Classification and Regression Trees (CART) algorithm, the efficient splitting of categorical features using standard criteria like GINI and Entropy is well-established. However, using the Mean Absolute Error (MAE) criterion for categorical features has traditionally relied on various numerical encoding methods. This paper demonstrates that unsupervised numerical encoding methods are not viable for the MAE criteria. Furthermore, we present a novel and efficient splitting algorithm that addresses the challenges of handling categorical features with the MAE criterion. Our findings underscore the limitations of existing approaches and offer a promising solution to enhance the handling of categorical data in CART algorithms.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Rectifying Regression in Reinforcement Learning
This paper investigates the impact of the loss function in value-based methods for reinforcement learning through an analysis of underlying prediction objectives. We theoretically show that mean absolute error is a bette…
Reinforcement LearningGeneralization Properties of Decision Trees on Real-valued and Categorical Features
We revisit binary decision trees from the perspective of partitions of the data. We introduce the notion of partitioning function, and we relate it to the growth function and to the VC dimension. We consider three types …
Measure Inducing Classification and Regression Trees for Functional Data
We propose a tree-based algorithm for classification and regression problems in the context of functional data analysis, which allows to leverage representation learning and multiple splitting rules at the node level, re…
ClassificationGeneral ClassificationregressionRepresentation LearningQuantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems
Regression problems have been widely studied in machinelearning literature resulting in a plethora of regression models and performance measures. However, there are few techniques specially dedicated to solve the problem…
regressionSpecificityVocal Bursts Intensity PredictionModeling Text with Decision Forests using Categorical-Set Splits
Decision forest algorithms typically model data by learning a binary tree structure recursively where every node splits the feature space into two sub-regions, sending examples into the left or right branch as a result. …
text-classificationText Classification