# 1. Introduction¶

## 1.1. API’s of imbalanced-learn samplers¶

The available samplers follows the scikit-learn API using the base estimator
and adding a sampling functionality through the `sample`

method:

- Estimator
The base object, implements a

`fit`

method to learn from data, either:estimator = obj.fit(data, targets)

- Resampler
To resample a data sets, each sampler implements:

data_resampled, targets_resampled = obj.fit_resample(data, targets)

Imbalanced-learn samplers accept the same inputs that in scikit-learn:

`data`

: array-like (2-D list, pandas.Dataframe, numpy.array) or sparse matrices;`targets`

: array-like (1-D list, pandas.Series, numpy.array).

The output will be of the following type:

`data_resampled`

: array-like (2-D list, pandas.Dataframe, numpy.array) orsparse matrices; *

`targets_resampled`

: 1-D numpy.array or pd.Series.

Sparse input

For sparse input the data is **converted to the Compressed Sparse Rows
representation** (see `scipy.sparse.csr_matrix`

) before being fed to the
sampler. To avoid unnecessary memory copies, it is recommended to choose the
CSR representation upstream.

## 1.2. Problem statement regarding imbalanced data sets¶

The learning phase and the subsequent prediction of machine learning algorithms can be affected by the problem of imbalanced data set. The balancing issue corresponds to the difference of the number of samples in the different classes. We illustrate the effect of training a linear SVM classifier with different level of class balancing.

As expected, the decision function of the linear SVM is highly impacted. With a greater imbalanced ratio, the decision function favor the class with the larger number of samples, usually referred as the majority class.