Note

Click here to download the full example code

# Metrics specific to imbalanced learning#

Specific metrics have been developed to evaluate classifier which
has been trained using imbalanced data. `imblearn`

provides mainly
two additional metrics which are not implemented in `sklearn`

: (i)
geometric mean and (ii) index balanced accuracy.

```
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# License: MIT
```

```
print(__doc__)
RANDOM_STATE = 42
```

First, we will generate some imbalanced dataset.

```
from sklearn.datasets import make_classification
X, y = make_classification(
n_classes=3,
class_sep=2,
weights=[0.1, 0.9],
n_informative=10,
n_redundant=1,
flip_y=0,
n_features=20,
n_clusters_per_class=4,
n_samples=5000,
random_state=RANDOM_STATE,
)
```

We will split the data into a training and testing set.

We will create a pipeline made of a `SMOTE`

over-sampler followed by a `LinearSVC`

classifier.

```
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
from imblearn.over_sampling import SMOTE
```

```
from imblearn.pipeline import make_pipeline
model = make_pipeline(
StandardScaler(),
SMOTE(random_state=RANDOM_STATE),
LinearSVC(max_iter=10_000, random_state=RANDOM_STATE),
)
```

Now, we will train the model on the training set and get the prediction
associated with the testing set. Be aware that the resampling will happen
only when calling `fit`

: the number of samples in `y_pred`

is the same than
in `y_test`

.

The geometric mean corresponds to the square root of the product of the sensitivity and specificity. Combining the two metrics should account for the balancing of the dataset.

```
from imblearn.metrics import geometric_mean_score
print(f"The geometric mean is {geometric_mean_score(y_test, y_pred):.3f}")
```

```
The geometric mean is 0.939
```

The index balanced accuracy can transform any metric to be used in imbalanced learning problems.

```
from imblearn.metrics import make_index_balanced_accuracy
alpha = 0.1
geo_mean = make_index_balanced_accuracy(alpha=alpha, squared=True)(geometric_mean_score)
print(
f"The IBA using alpha={alpha} and the geometric mean: "
f"{geo_mean(y_test, y_pred):.3f}"
)
```

```
The IBA using alpha=0.1 and the geometric mean: 0.882
```

```
alpha = 0.5
geo_mean = make_index_balanced_accuracy(alpha=alpha, squared=True)(geometric_mean_score)
print(
f"The IBA using alpha={alpha} and the geometric mean: "
f"{geo_mean(y_test, y_pred):.3f}"
)
```

```
The IBA using alpha=0.5 and the geometric mean: 0.882
```

**Total running time of the script:** ( 0 minutes 2.652 seconds)

**Estimated memory usage:** 8 MB