Talip7/scikit-learn-issues-multilabel huggingface dataset:
Visual improvements for ROC and precision-recall plots

### Describe the workflow you want to enable

Hello,

I would like to suggest the following improvements:

- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top and right lines to see the curve better when values are close to 1, or plot the frame with a dotted line (#26367)
- [x] (3). Fix aspect ratio to squared, since the two axes are the same scale. (#26366)
- [x] (4). Add title (not planned)

### Describe your proposed solution

Hello,

While working on ROC and precision-recall plots, I came up with the following suggestions:

1. Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1
2. Modify the plotting frame: either remove the top and right lines to see the curve better when values are close to 1, or plot the frame with a dotted line
3. Fix aspect ratio to squared, since the two axes are the same scale.
4. Add title

```
        ax.set_xlim((-0.01, 1.01))
        ax.set_ylim((-0.01, 1.01))
        for s in ['right', 'left', 'top', 'bottom']:
            ax.spines[s].set_visible(False)
        ax.axvline(1, color='black', linewidth=0.5, ls='dotted')
        ax.axhline(1, color='black', linewidth=0.5, ls='dotted')
        ax.axvline(0, color='black', linewidth=0.5, ls='dotted')
        ax.axhline(0, color='black', linewidth=0.5, ls='dotted')
        ax.set_aspect(1)
        ax.set_title(f'Precision-Recall Curve')
```

### Example 1 Edge case
#### PR
Before:
![image](https://user-images.githubusercontent.com/1184396/226746221-40b40d56-74a4-49c1-82aa-4a3f3366abe2.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746245-3e0a0ffd-28f0-4851-8a41-04629750bd2c.png)

#### ROC
Before:
![image](https://user-images.githubusercontent.com/1184396/226746270-ecc2a1ce-48dc-4682-8acd-86a4d06dfcf0.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746289-8ba8c906-f8f8-4d74-9269-b12c16d339fa.png)

### Example 2 - Edge case

#### PR
Before:
![image](https://user-images.githubusercontent.com/1184396/226745897-de69cf76-f520-499f-b8b6-4e9ede9e7ba2.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226745936-5fc34fff-eff3-4a70-8b99-b7c731984723.png)

#### ROC
Before:
![image](https://user-images.githubusercontent.com/1184396/226745998-d1788d4a-fd0a-4293-bef5-170a71c0a098.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746031-e93dff40-2487-4776-a5b8-baeef9a36e5d.png)



### Describe alternatives you've considered, if relevant

_No response_

### Additional context

_No response_

COMMENT:
@Charlie-XIAO Actually I am the culprit here. We are currently in a coding sprint and I suggested this item #3 since this is pretty much independent from others and should not raise any conflict with the previous PRs.

Do you have changes locally that you would like to submit? I think that we can already start to review them and solving potential conflicts should be fine because they would be quite restricted.

Labels: Enhancement
Visual improvements for ROC and precision-recall plots

### Describe the workflow you want to enable

Hello,

I would like to suggest the following improvements:

- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top and right lines to see the curve better when values are close to 1, or plot the frame with a dotted line (#26367)
- [x] (3). Fix aspect ratio to squared, since the two axes are the same scale. (#26366)
- [x] (4). Add title (not planned)

### Describe your proposed solution

Hello,

While working on ROC and precision-recall plots, I came up with the following suggestions:

1. Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1
2. Modify the plotting frame: either remove the top and right lines to see the curve better when values are close to 1, or plot the frame with a dotted line
3. Fix aspect ratio to squared, since the two axes are the same scale.
4. Add title

```
        ax.set_xlim((-0.01, 1.01))
        ax.set_ylim((-0.01, 1.01))
        for s in ['right', 'left', 'top', 'bottom']:
            ax.spines[s].set_visible(False)
        ax.axvline(1, color='black', linewidth=0.5, ls='dotted')
        ax.axhline(1, color='black', linewidth=0.5, ls='dotted')
        ax.axvline(0, color='black', linewidth=0.5, ls='dotted')
        ax.axhline(0, color='black', linewidth=0.5, ls='dotted')
        ax.set_aspect(1)
        ax.set_title(f'Precision-Recall Curve')
```

### Example 1 Edge case
#### PR
Before:
![image](https://user-images.githubusercontent.com/1184396/226746221-40b40d56-74a4-49c1-82aa-4a3f3366abe2.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746245-3e0a0ffd-28f0-4851-8a41-04629750bd2c.png)

#### ROC
Before:
![image](https://user-images.githubusercontent.com/1184396/226746270-ecc2a1ce-48dc-4682-8acd-86a4d06dfcf0.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746289-8ba8c906-f8f8-4d74-9269-b12c16d339fa.png)

### Example 2 - Edge case

#### PR
Before:
![image](https://user-images.githubusercontent.com/1184396/226745897-de69cf76-f520-499f-b8b6-4e9ede9e7ba2.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226745936-5fc34fff-eff3-4a70-8b99-b7c731984723.png)

#### ROC
Before:
![image](https://user-images.githubusercontent.com/1184396/226745998-d1788d4a-fd0a-4293-bef5-170a71c0a098.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746031-e93dff40-2487-4776-a5b8-baeef9a36e5d.png)



### Describe alternatives you've considered, if relevant

_No response_

### Additional context

_No response_

COMMENT:
@glemaitre This example looks really nice! I will make modifications for that. But in this case, should we not fix the axes to [0, 1]? Perhaps [-0.05, 1.05] instead or just leave it for `matplotlib` to decide?

Labels: Enhancement
Visual improvements for ROC and precision-recall plots

### Describe the workflow you want to enable

Hello,

I would like to suggest the following improvements:

- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top and right lines to see the curve better when values are close to 1, or plot the frame with a dotted line (#26367)
- [x] (3). Fix aspect ratio to squared, since the two axes are the same scale. (#26366)
- [x] (4). Add title (not planned)

### Describe your proposed solution

Hello,

While working on ROC and precision-recall plots, I came up with the following suggestions:

1. Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1
2. Modify the plotting frame: either remove the top and right lines to see the curve better when values are close to 1, or plot the frame with a dotted line
3. Fix aspect ratio to squared, since the two axes are the same scale.
4. Add title

```
        ax.set_xlim((-0.01, 1.01))
        ax.set_ylim((-0.01, 1.01))
        for s in ['right', 'left', 'top', 'bottom']:
            ax.spines[s].set_visible(False)
        ax.axvline(1, color='black', linewidth=0.5, ls='dotted')
        ax.axhline(1, color='black', linewidth=0.5, ls='dotted')
        ax.axvline(0, color='black', linewidth=0.5, ls='dotted')
        ax.axhline(0, color='black', linewidth=0.5, ls='dotted')
        ax.set_aspect(1)
        ax.set_title(f'Precision-Recall Curve')
```

### Example 1 Edge case
#### PR
Before:
![image](https://user-images.githubusercontent.com/1184396/226746221-40b40d56-74a4-49c1-82aa-4a3f3366abe2.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746245-3e0a0ffd-28f0-4851-8a41-04629750bd2c.png)

#### ROC
Before:
![image](https://user-images.githubusercontent.com/1184396/226746270-ecc2a1ce-48dc-4682-8acd-86a4d06dfcf0.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746289-8ba8c906-f8f8-4d74-9269-b12c16d339fa.png)

### Example 2 - Edge case

#### PR
Before:
![image](https://user-images.githubusercontent.com/1184396/226745897-de69cf76-f520-499f-b8b6-4e9ede9e7ba2.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226745936-5fc34fff-eff3-4a70-8b99-b7c731984723.png)

#### ROC
Before:
![image](https://user-images.githubusercontent.com/1184396/226745998-d1788d4a-fd0a-4293-bef5-170a71c0a098.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746031-e93dff40-2487-4776-a5b8-baeef9a36e5d.png)



### Describe alternatives you've considered, if relevant

_No response_

### Additional context

_No response_

COMMENT:
Indeed, we need to have an offset of `0.001` as well. Otherwise, matplotlib will crop our curve.

Labels: Enhancement
Visual improvements for ROC and precision-recall plots

### Describe the workflow you want to enable

Hello,

I would like to suggest the following improvements:

- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top and right lines to see the curve better when values are close to 1, or plot the frame with a dotted line (#26367)
- [x] (3). Fix aspect ratio to squared, since the two axes are the same scale. (#26366)
- [x] (4). Add title (not planned)

### Describe your proposed solution

Hello,

While working on ROC and precision-recall plots, I came up with the following suggestions:

1. Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1
2. Modify the plotting frame: either remove the top and right lines to see the curve better when values are close to 1, or plot the frame with a dotted line
3. Fix aspect ratio to squared, since the two axes are the same scale.
4. Add title

```
        ax.set_xlim((-0.01, 1.01))
        ax.set_ylim((-0.01, 1.01))
        for s in ['right', 'left', 'top', 'bottom']:
            ax.spines[s].set_visible(False)
        ax.axvline(1, color='black', linewidth=0.5, ls='dotted')
        ax.axhline(1, color='black', linewidth=0.5, ls='dotted')
        ax.axvline(0, color='black', linewidth=0.5, ls='dotted')
        ax.axhline(0, color='black', linewidth=0.5, ls='dotted')
        ax.set_aspect(1)
        ax.set_title(f'Precision-Recall Curve')
```

### Example 1 Edge case
#### PR
Before:
![image](https://user-images.githubusercontent.com/1184396/226746221-40b40d56-74a4-49c1-82aa-4a3f3366abe2.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746245-3e0a0ffd-28f0-4851-8a41-04629750bd2c.png)

#### ROC
Before:
![image](https://user-images.githubusercontent.com/1184396/226746270-ecc2a1ce-48dc-4682-8acd-86a4d06dfcf0.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746289-8ba8c906-f8f8-4d74-9269-b12c16d339fa.png)

### Example 2 - Edge case

#### PR
Before:
![image](https://user-images.githubusercontent.com/1184396/226745897-de69cf76-f520-499f-b8b6-4e9ede9e7ba2.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226745936-5fc34fff-eff3-4a70-8b99-b7c731984723.png)

#### ROC
Before:
![image](https://user-images.githubusercontent.com/1184396/226745998-d1788d4a-fd0a-4293-bef5-170a71c0a098.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746031-e93dff40-2487-4776-a5b8-baeef9a36e5d.png)



### Describe alternatives you've considered, if relevant

_No response_

### Additional context

_No response_

COMMENT:
Okay, that might be something @mrastgoo need to take care of. I will work on https://github.com/scikit-learn/scikit-learn/issues/25929#issuecomment-1546903171

Labels: Enhancement
Visual improvements for ROC and precision-recall plots

### Describe the workflow you want to enable

Hello,

I would like to suggest the following improvements:

- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top and right lines to see the curve better when values are close to 1, or plot the frame with a dotted line (#26367)
- [x] (3). Fix aspect ratio to squared, since the two axes are the same scale. (#26366)
- [x] (4). Add title (not planned)

### Describe your proposed solution

Hello,

While working on ROC and precision-recall plots, I came up with the following suggestions:

1. Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1
2. Modify the plotting frame: either remove the top and right lines to see the curve better when values are close to 1, or plot the frame with a dotted line
3. Fix aspect ratio to squared, since the two axes are the same scale.
4. Add title

```
        ax.set_xlim((-0.01, 1.01))
        ax.set_ylim((-0.01, 1.01))
        for s in ['right', 'left', 'top', 'bottom']:
            ax.spines[s].set_visible(False)
        ax.axvline(1, color='black', linewidth=0.5, ls='dotted')
        ax.axhline(1, color='black', linewidth=0.5, ls='dotted')
        ax.axvline(0, color='black', linewidth=0.5, ls='dotted')
        ax.axhline(0, color='black', linewidth=0.5, ls='dotted')
        ax.set_aspect(1)
        ax.set_title(f'Precision-Recall Curve')
```

### Example 1 Edge case
#### PR
Before:
![image](https://user-images.githubusercontent.com/1184396/226746221-40b40d56-74a4-49c1-82aa-4a3f3366abe2.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746245-3e0a0ffd-28f0-4851-8a41-04629750bd2c.png)

#### ROC
Before:
![image](https://user-images.githubusercontent.com/1184396/226746270-ecc2a1ce-48dc-4682-8acd-86a4d06dfcf0.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746289-8ba8c906-f8f8-4d74-9269-b12c16d339fa.png)

### Example 2 - Edge case

#### PR
Before:
![image](https://user-images.githubusercontent.com/1184396/226745897-de69cf76-f520-499f-b8b6-4e9ede9e7ba2.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226745936-5fc34fff-eff3-4a70-8b99-b7c731984723.png)

#### ROC
Before:
![image](https://user-images.githubusercontent.com/1184396/226745998-d1788d4a-fd0a-4293-bef5-170a71c0a098.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746031-e93dff40-2487-4776-a5b8-baeef9a36e5d.png)



### Describe alternatives you've considered, if relevant

_No response_

### Additional context

_No response_

COMMENT:
Precision-Recall curves should not be linearly interpolated. C.f.

> Davis, J., & Goadrich, M. (2006, June). The relationship between Precision-Recall and ROC curves. In Proceedings of the 23rd international conference on Machine learning (pp. 233-240).

and

> Flach, P., & Kull, M. (2015). Precision-recall-gain curves: PR analysis done right. Advances in neural information processing systems, 28.

Labels: Enhancement
Visual improvements for ROC and precision-recall plots

### Describe the workflow you want to enable

Hello,

I would like to suggest the following improvements:

- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top and right lines to see the curve better when values are close to 1, or plot the frame with a dotted line (#26367)
- [x] (3). Fix aspect ratio to squared, since the two axes are the same scale. (#26366)
- [x] (4). Add title (not planned)

### Describe your proposed solution

Hello,

While working on ROC and precision-recall plots, I came up with the following suggestions:

1. Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1
2. Modify the plotting frame: either remove the top and right lines to see the curve better when values are close to 1, or plot the frame with a dotted line
3. Fix aspect ratio to squared, since the two axes are the same scale.
4. Add title

```
        ax.set_xlim((-0.01, 1.01))
        ax.set_ylim((-0.01, 1.01))
        for s in ['right', 'left', 'top', 'bottom']:
            ax.spines[s].set_visible(False)
        ax.axvline(1, color='black', linewidth=0.5, ls='dotted')
        ax.axhline(1, color='black', linewidth=0.5, ls='dotted')
        ax.axvline(0, color='black', linewidth=0.5, ls='dotted')
        ax.axhline(0, color='black', linewidth=0.5, ls='dotted')
        ax.set_aspect(1)
        ax.set_title(f'Precision-Recall Curve')
```

### Example 1 Edge case
#### PR
Before:
![image](https://user-images.githubusercontent.com/1184396/226746221-40b40d56-74a4-49c1-82aa-4a3f3366abe2.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746245-3e0a0ffd-28f0-4851-8a41-04629750bd2c.png)

#### ROC
Before:
![image](https://user-images.githubusercontent.com/1184396/226746270-ecc2a1ce-48dc-4682-8acd-86a4d06dfcf0.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746289-8ba8c906-f8f8-4d74-9269-b12c16d339fa.png)

### Example 2 - Edge case

#### PR
Before:
![image](https://user-images.githubusercontent.com/1184396/226745897-de69cf76-f520-499f-b8b6-4e9ede9e7ba2.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226745936-5fc34fff-eff3-4a70-8b99-b7c731984723.png)

#### ROC
Before:
![image](https://user-images.githubusercontent.com/1184396/226745998-d1788d4a-fd0a-4293-bef5-170a71c0a098.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746031-e93dff40-2487-4776-a5b8-baeef9a36e5d.png)



### Describe alternatives you've considered, if relevant

_No response_

### Additional context

_No response_

COMMENT:
@kno10 that is correct. But to which specific features are you referring? As far as I know, we don't linearly interpolate for both the plotting and the computation of the average precision.

I might have missed something though.

Labels: Enhancement
Visual improvements for ROC and precision-recall plots

### Describe the workflow you want to enable

Hello,

I would like to suggest the following improvements:

- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top and right lines to see the curve better when values are close to 1, or plot the frame with a dotted line (#26367)
- [x] (3). Fix aspect ratio to squared, since the two axes are the same scale. (#26366)
- [x] (4). Add title (not planned)

### Describe your proposed solution

Hello,

While working on ROC and precision-recall plots, I came up with the following suggestions:

1. Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1
2. Modify the plotting frame: either remove the top and right lines to see the curve better when values are close to 1, or plot the frame with a dotted line
3. Fix aspect ratio to squared, since the two axes are the same scale.
4. Add title

```
        ax.set_xlim((-0.01, 1.01))
        ax.set_ylim((-0.01, 1.01))
        for s in ['right', 'left', 'top', 'bottom']:
            ax.spines[s].set_visible(False)
        ax.axvline(1, color='black', linewidth=0.5, ls='dotted')
        ax.axhline(1, color='black', linewidth=0.5, ls='dotted')
        ax.axvline(0, color='black', linewidth=0.5, ls='dotted')
        ax.axhline(0, color='black', linewidth=0.5, ls='dotted')
        ax.set_aspect(1)
        ax.set_title(f'Precision-Recall Curve')
```

### Example 1 Edge case
#### PR
Before:
![image](https://user-images.githubusercontent.com/1184396/226746221-40b40d56-74a4-49c1-82aa-4a3f3366abe2.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746245-3e0a0ffd-28f0-4851-8a41-04629750bd2c.png)

#### ROC
Before:
![image](https://user-images.githubusercontent.com/1184396/226746270-ecc2a1ce-48dc-4682-8acd-86a4d06dfcf0.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746289-8ba8c906-f8f8-4d74-9269-b12c16d339fa.png)

### Example 2 - Edge case

#### PR
Before:
![image](https://user-images.githubusercontent.com/1184396/226745897-de69cf76-f520-499f-b8b6-4e9ede9e7ba2.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226745936-5fc34fff-eff3-4a70-8b99-b7c731984723.png)

#### ROC
Before:
![image](https://user-images.githubusercontent.com/1184396/226745998-d1788d4a-fd0a-4293-bef5-170a71c0a098.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746031-e93dff40-2487-4776-a5b8-baeef9a36e5d.png)



### Describe alternatives you've considered, if relevant

_No response_

### Additional context

_No response_

COMMENT:
The stepwise interpolation in the plotting is overestimating the area, too. The resulting area is average precision, not AU-PRC.

Labels: Enhancement
Visual improvements for ROC and precision-recall plots

### Describe the workflow you want to enable

Hello,

I would like to suggest the following improvements:

- [x] (1) Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1 (#26366)
- [x] (2) Modify the plotting frame: either remove the top and right lines to see the curve better when values are close to 1, or plot the frame with a dotted line (#26367)
- [x] (3). Fix aspect ratio to squared, since the two axes are the same scale. (#26366)
- [x] (4). Add title (not planned)

### Describe your proposed solution

Hello,

While working on ROC and precision-recall plots, I came up with the following suggestions:

1. Add x and y axis limit: [0, 1], in sklearn axes currently start at ~-0.1
2. Modify the plotting frame: either remove the top and right lines to see the curve better when values are close to 1, or plot the frame with a dotted line
3. Fix aspect ratio to squared, since the two axes are the same scale.
4. Add title

```
        ax.set_xlim((-0.01, 1.01))
        ax.set_ylim((-0.01, 1.01))
        for s in ['right', 'left', 'top', 'bottom']:
            ax.spines[s].set_visible(False)
        ax.axvline(1, color='black', linewidth=0.5, ls='dotted')
        ax.axhline(1, color='black', linewidth=0.5, ls='dotted')
        ax.axvline(0, color='black', linewidth=0.5, ls='dotted')
        ax.axhline(0, color='black', linewidth=0.5, ls='dotted')
        ax.set_aspect(1)
        ax.set_title(f'Precision-Recall Curve')
```

### Example 1 Edge case
#### PR
Before:
![image](https://user-images.githubusercontent.com/1184396/226746221-40b40d56-74a4-49c1-82aa-4a3f3366abe2.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746245-3e0a0ffd-28f0-4851-8a41-04629750bd2c.png)

#### ROC
Before:
![image](https://user-images.githubusercontent.com/1184396/226746270-ecc2a1ce-48dc-4682-8acd-86a4d06dfcf0.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746289-8ba8c906-f8f8-4d74-9269-b12c16d339fa.png)

### Example 2 - Edge case

#### PR
Before:
![image](https://user-images.githubusercontent.com/1184396/226745897-de69cf76-f520-499f-b8b6-4e9ede9e7ba2.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226745936-5fc34fff-eff3-4a70-8b99-b7c731984723.png)

#### ROC
Before:
![image](https://user-images.githubusercontent.com/1184396/226745998-d1788d4a-fd0a-4293-bef5-170a71c0a098.png)

After:
![image](https://user-images.githubusercontent.com/1184396/226746031-e93dff40-2487-4776-a5b8-baeef9a36e5d.png)



### Describe alternatives you've considered, if relevant

_No response_

### Additional context

_No response_

COMMENT:
OK, I think I see what you mean. I check the paper and I am certain that the output of the `precision_recall_curve` is fine (we are have every increment of True Positive in it).

So regarding the visual aspect, if we show the Average Precision in the legend, it makes sense to show the stepwise interpolation while if we want to show the AU-PRC, then we need to use linear interpolation between each threshold.

Labels: Enhancement