Dataset meta-level and statistical features affect machine learning performance.

ABSTRACT: What dataset features affect machine learning (ML) performance has primarily been unknown in the current literature. This study examines the impact of tabular datasets' different meta-level and statistical features on the performance of various ML algorithms. The three meta-level features this study considered are the dataset size, the number of attributes and the ratio between the positive (class 1) and negative (class 0) class instances. It considered four statistical features for each dataset: mean, standard deviation, skewness and kurtosis. After applying the required scaling, this study averaged (uniform and weighted) each dataset's different attributes to quantify its four statistical features. We analysed 200 open-access tabular datasets from the Kaggle (147) and UCI Machine Learnin

SUBMITTER: Uddin S 

PROVIDER: S-EPMC10796674 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature