
## On Truthing Issues in Supervised Classification
Jonathan K. Su
Keywords: 
JMLR/2024/Proceedings/19301 - On Truthing Issues in Supervised Classification.pdf
Project URL: nan

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[10]

Implementation not given.

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[5]

(2/2)

The authors use simulated data in section 5 and provide specifications/formula and the parameter values. Implementation not given. In section 5.4 they use the Ionosphere binary classification data sets and provide a direct link to the UCI datasets in the references, the dataset is described and some statistics are given.

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[10]

Parameters unclear.

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[1]

The authors state the data set split in 5.4. The authors measure accuracy and P/R/ROC. Variation in std dev. Figure 24 is on the test set.

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[9]

Requires expertise on supervised classification.
