> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# roc_curve()
export const GitHubLink = ({url}) =>
GitHub source
;
### function `roc_curve`
```python theme={null}
roc_curve(
y_true: 'Sequence[numbers.Number]',
y_probas: 'Sequence[Sequence[float]] | None' = None,
labels: 'list[str] | None' = None,
classes_to_plot: 'list[numbers.Number] | None' = None,
title: 'str' = 'ROC Curve',
split_table: 'bool' = False
) → CustomChart
```
Constructs Receiver Operating Characteristic (ROC) curve chart.
**Args:**
* `y_true`: The true class labels (ground truth) for the target variable. Shape should be (num\_samples,).
* `y_probas`: The predicted probabilities or decision scores for each class. Shape should be (num\_samples, num\_classes).
* `labels`: Human-readable labels corresponding to the class indices in `y_true`. For example, if `labels=['dog', 'cat']`, class 0 will be displayed as 'dog' and class 1 as 'cat' in the plot. If None, the raw class indices from `y_true` will be used. Default is None.
* `classes_to_plot`: A subset of unique class labels to include in the ROC curve. If None, all classes in `y_true` will be plotted. Default is None.
* `title`: Title of the ROC curve plot. Default is "ROC Curve".
* `split_table`: Whether the table should be split into a separate section in the W\&B UI. If `True`, the table will be displayed in a section named "Custom Chart Tables". Default is `False`.
**Returns:**
* `CustomChart`: A custom chart object that can be logged to W\&B. To log the chart, pass it to `wandb.log()`.
**Raises:**
* `wandb.Error`: If numpy, pandas, or scikit-learn are not found.
**Example:**
```python theme={null}
import numpy as np
import wandb
# Simulate a medical diagnosis classification problem with three diseases
n_samples = 200
n_classes = 3
# True labels: assign "Diabetes", "Hypertension", or "Heart Disease" to
# each sample
disease_labels = ["Diabetes", "Hypertension", "Heart Disease"]
# 0: Diabetes, 1: Hypertension, 2: Heart Disease
y_true = np.random.choice([0, 1, 2], size=n_samples)
# Predicted probabilities: simulate predictions, ensuring they sum to 1
# for each sample
y_probas = np.random.dirichlet(np.ones(n_classes), size=n_samples)
# Specify classes to plot (plotting all three diseases)
classes_to_plot = [0, 1, 2]
# Initialize a W&B run and log a ROC curve plot for disease classification
with wandb.init(project="medical_diagnosis") as run:
roc_plot = wandb.plot.roc_curve(
y_true=y_true,
y_probas=y_probas,
labels=disease_labels,
classes_to_plot=classes_to_plot,
title="ROC Curve for Disease Classification",
)
run.log({"roc-curve": roc_plot})
```