# Plot probability scores

by: Chris Cave

(Feyn version 3.1.0 or newer)

The output of a binary classifier can be interpreted as the predicted probability of a sample belonging to the positive class. It is useful to see the distribution of scores to evaluate the quality of the classifier.

*This plot only works for classifiers, and will return a type error if you try to use it on a regressor.*

## Example

```
import feyn
import pandas as pd
from sklearn.datasets import load_breast_cancer
# Load into a pandas dataframe
breast_cancer = load_breast_cancer(as_frame=True)
data = breast_cancer.frame
# Train/test split
train, test = feyn.tools.split(data, ratio=[0.6, 0.4], stratify='target', random_state=666)
# Instantiate a QLattice
ql = feyn.QLattice()
models = ql.auto_run(train, 'target', 'classification')
best = models[0]
```

### Plotting the probability scores

The function `plot_probability_scores`

plots a histogram of the probabilities of the passed dataset.

```
best.plot_probability_scores(test)
```

The different colours highlight the true classes of the samples. A good classifier would have a clear separation of the negative and positive class.

### Customising the plot labels

You can customise the labels of the positive and negative class with your own labels, by supplying them to the `legend`

parameter. You can also adjust the positioning of the legend.

The legend array goes in the order: `Positive`

, `Negative`

.

```
best.plot_probability_scores(test, legend=["Benign", "Malignant"], legend_loc="upper left")
```

### Saving the plot

You can save the plot using the `filename`

parameter. The plot is saved in the current working directory unless another path specifed.

```
best.plot_probability_scores(data=train, filename="feyn-plot")
```

If the extension is not specified then it is saved as a png file.

`Feyn`

Location in This function can also be found in `feyn.plots`

module.

```
from feyn.plots import plot_probability_scores
y_true = train['target']
y_pred = best.predict(train)
plot_probability_scores(y_true, y_pred)
```