Note

Click here to download the full example code

# Metrics specific to imbalanced learningΒΆ

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
from sklearn import datasets
from sklearn.svm import LinearSVC
from sklearn.model_selection import train_test_split
from imblearn import over_sampling as os
from imblearn import pipeline as pl
from imblearn.metrics import (geometric_mean_score,
make_index_balanced_accuracy)
print(__doc__)
RANDOM_STATE = 42
# Generate a dataset
X, y = datasets.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)
pipeline = pl.make_pipeline(os.SMOTE(random_state=RANDOM_STATE),
LinearSVC(random_state=RANDOM_STATE))
# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y,
random_state=RANDOM_STATE)
# Train the classifier with balancing
pipeline.fit(X_train, y_train)
# Test the classifier and get the prediction
y_pred_bal = pipeline.predict(X_test)
```

Out:

```
/home/circleci/miniconda/envs/testenv/lib/python3.7/site-packages/sklearn/svm/_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
"the number of iterations.", ConvergenceWarning)
```

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.

```
print('The geometric mean is {}'.format(geometric_mean_score(
y_test,
y_pred_bal)))
```

Out:

```
The geometric mean is 0.9262633940760341
```

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

```
alpha = 0.1
geo_mean = make_index_balanced_accuracy(alpha=alpha, squared=True)(
geometric_mean_score)
print('The IBA using alpha = {} and the geometric mean: {}'.format(
alpha, geo_mean(
y_test,
y_pred_bal)))
alpha = 0.5
geo_mean = make_index_balanced_accuracy(alpha=alpha, squared=True)(
geometric_mean_score)
print('The IBA using alpha = {} and the geometric mean: {}'.format(
alpha, geo_mean(
y_test,
y_pred_bal)))
```

Out:

```
The IBA using alpha = 0.1 and the geometric mean: 0.8579638752052544
The IBA using alpha = 0.5 and the geometric mean: 0.8579638752052544
```

**Total running time of the script:** ( 0 minutes 1.631 seconds)

**Estimated memory usage:** 8 MB