Name
Title | ||
|---|---|---|
Phi-Delta-Diagrams: Software Implementation Of A Visual Tool For Assessing Classifier And Feature Performance |
Abstract | ||
|---|---|---|
In this article, a two-tiered 2D tool is described, called <phi,delta > diagrams, and this tool has been devised to support the assessment of classifiers in terms of accuracy and bias. In their standard versions, these diagrams provide information, as the underlying data were in fact balanced. Their generalization, i.e., ability to account for the imbalance, will be also briefly described. In either case, the isometrics of accuracy and bias are immediately evident therein, as-according to a specific design choice-they are in fact straight lines parallel to the x-axis and y-axis, respectively. <phi,delta > diagrams can also be used to assess the importance of features, as highly discriminant ones are immediately evident therein. In this paper, a comprehensive introduction on how to adopt <phi,delta > diagrams as a standard tool for classifier and feature assessment is given. In particular, with the goal of illustrating all relevant details from a pragmatic perspective, their implementation and usage as Python and R packages will be described. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/make1010007 | MACHINE LEARNING AND KNOWLEDGE EXTRACTION |
Keywords | DocType | Volume |
feature importance, classifier performance measures, confusion matrices, ROC curves, R-package, Python package | Journal | 1 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
11 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giuliano Armano | 1 | 325 | 42.89 |
| Alessandro Giuliani | 2 | 170 | 25.21 |
| Ursula Neumann | 3 | 0 | 0.34 |
| Nikolas Rothe | 4 | 0 | 0.34 |
| Dominik Heider | 5 | 0 | 0.34 |