Results 21 to 30 of about 7,127,658 (298)

Inherent Inconsistencies of Feature Importance

CoRR, 2022
The rapid advancement and widespread adoption of machine learning-driven technologies have underscored the practical and ethical need for creating interpretable artificial intelligence systems. Feature importance, a method that assigns scores to the contribution of individual features on prediction outcomes, seeks to bridge this gap as a tool for ...
Nimrod Harel, Ran Gilad-Bachrach, Uri Obolski   +2 more
openaire   +2 more sources

Feature importance.

, 2023
Contains the numbers of the feature importance plot (Fig 3 - bottom right). (CSV)
Bence Supola (14581155), Arnold Baca (848816), Thomas Hoch (14581158)   +2 more
core   +1 more source

Grouped feature importance and combined features effect plot

Data Mining and Knowledge Discovery, 2022
Abstract Interpretable machine learning has become a very active area of research due to the rising popularity of machine learning algorithms and their inherently challenging interpretability. Most work in this area has been focused on the interpretation of single features in a model.
Quay Au, Julia Herbinger, Clemens Stachl, Bernd Bischl, Giuseppe Casalicchio   +4 more
openaire   +4 more sources

Feature Importance (Feature_Importance_/Dataset2).

, 2023
Feature Importance (Feature_Importance_/Dataset2).
Yongxian Fan (8849987), Guicong Sun (17020804), Meng Liu (261774)   +2 more
core   +1 more source

Feature selection from an interaction perspective: a review of methods, advances, and challenges [PDF]

PeerJ Computer Science
Feature selection plays a vital role in machine learning and data mining by identifying a representative subset to improve model performance, reduce computational cost, and prevent overfitting.
Shuifei Zheng, Bin Nie, Zhiyao Zhang, Xiang Li   +3 more
doaj   +2 more sources

Modular feature selection using relative importance factors [PDF]

, 2004
Feature selection plays an important role in finding relevant or irrelevant features in classification. Genetic algorithms (GAs) have been used as conventional methods for classifiers to adaptively evolve solutions for classification problems.
Li, P, Guan, SU, Zhu, F
core   +1 more source

Feature Importance (Feature_Importance_/ Dataset1).

, 2023
Feature Importance (Feature_Importance_/ Dataset1).
Yongxian Fan (8849987), Guicong Sun (17020804), Meng Liu (261774)   +2 more
core   +1 more source

Conditional feature importance for mixed data

AStA Advances in Statistical Analysis, 2023
AbstractDespite the popularity of feature importance (FI) measures in interpretable machine learning, the statistical adequacy of these methods is rarely discussed. From a statistical perspective, a major distinction is between analysing a variable’s importance before and after adjusting for covariates—i.e., between marginal and conditional measures ...
Kristin Blesch, David S. Watson, Marvin N. Wright   +2 more
openaire   +7 more sources

Feature importance as determined by recursive feature elimination method.

, 2022
Feature importance as determined by recursive feature elimination method.
E. R. Anirudh (12769992), R. Sivakumar (9265), Sai Viswanth Reddy Y. (12769995), Rahul Soangra (12769989), Emmanuel B. John (12769998)   +4 more
core   +1 more source

Feature importance (Importance) and p values for features that achieved a feature importance greater than 0.1% in the Chinese RF.

, 2023
Feature importance (Importance) and p values for features that achieved a feature importance greater than 0.1% in the Chinese RF.
Shigeto Kawahara (3183039), Alexander James Kilpatrick (14328725), Aleksandra Ćwiek (14328728)   +2 more
core   +2 more sources