Repository: Giskard-AI/giskard-oss
Issue: [GSK-989] doc related issues (xhr)
Root cause: Documentation Deficiency

rabah-khalek:

* [ ] It’s not clear that `classification_label` are computed automatically, and which element of an array is the lowest/highest probability
* [ ] For optional argument in dataset and model, we should describe what the optional parameter are (example : classification_label)

@kevinmessiaen :

* [X] `DataQuality` can’t be resolved from `giskard`
  * [X] if he skipped step 4, he tried to look for `DataQuality` in our doc, we need to put some link between step 4 and 5
* [X] We should make sure that our snippet work without relying on previous snippet
* [ ] He’s not sure wether to use the decorator or the class to create a custom test
  * [X] \~he preferred to use a decorator\~
* [X] We should not put in the documentation the custom test part at first. It should more hidden. It’s hard for the user to understand what custom means
* [ ] He’s not sure about what are the parameters of `run` and think that we can only test the same model
* [X] doc: test_f1(actual_dataset,…) —> test_f1(dataset,…)
* [X] This copied snippet gave him an error: ⇒ it’s not the latest version of GitBook doc that has been copied 😕

```
print(f"Result: {passed}")print(f"F1: {results['f1'].passed} {results['f1'].metric}")print(f"DataQuality: {results['quality'].passed} {results['quality'].metric}")
```

* [ ] we should work on documenting the different input scenario to test-suite, it was difficult for him to know what to do without hints from JM: we should have a section title in guide called : “Compare 2 models with a suite”, “compare two 2 dataset with a suite”
* [ ] In the section **Apply your slicing function to a dataset**[**#**](https://giskard.readthedocs.io/en/latest/guides/create-slices-and-transformation-functions/index.html#apply-your-slicing-function-to-a-dataset)**,  w**e need to say we can convert df to pandas df
* [X] In the chaining part, what is the "WHY?" of the section "chain slices and transformation". The guide should be functional
* [X] In the chaining part, why do we hava
  * [X] the method add_transformation instead of transform
  * [X] the method add_slicing_function instead of slice
  * [X] the process method. What's the use?

<sub>From [SyncLinear.com](https://synclinear.com) | [GSK-989](https://linear.app/giskard/issue/GSK-989/doc-related-issues-xhr)</sub>

@jmsquare : is there a way to enlarge "warnings" and "hints" on sphinx?

<!-- From rabah on Linear -->

put:

```
In the case of binary classification, an array of probabilities   is also accepted. Make sure that the probability provided is for the first label provided in classification_labels.
```

next to classification_labels.

Stress on the fact that `classification_labels` and `classification_threshold` are relevant only for classification

use iris as example for snippets in model and dataset ([https://rasbt.github.io/mlxtend/user_guide/data/iris_data/](https://rasbt.github.io/mlxtend/user_guide/data/iris_data/)):

```

iris = datasets.load_iris()
df_iris = pd.DataFrame(iris.data,columns=iris.feature_names)

d = {0: 'setosa', 1: 'versicolor', 2: 'virginica'}
```

Classfication_labels:
Type: dict or a list
The mapping of your classification labels. It’s a dictionnary where the

* Keys are values in the target variable of the dataset you upload in Giskard
* Values are human-readable labels


If the keys are already human readable you can simply use put your classification labels as list of string

<!-- From rabah on Linear -->

add tabs for each of:

* multilabel classification
* binary classfication
* regression

in the guides and only highlight relevant parameters

<!-- From rabah on Linear -->

@rabah-khalek-khalek is this card still in progress?

<!-- From Andrey Avtomonov on Linear -->

What needs to be implemented was already implemented here.