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# 3. Data Preprocessing¶

## 3.1. Normalisation¶

If a dataset contains features with vastly different scales, it is advisible to normalise the features first. There are a few option:

• Normalise the features to zero mean and unit variance.
• Normalise the features to unit variance.
• Normalise the features to unit interval.

## 3.2. Balanced Train-Test Split¶

Often, the class distribution in a dataset is not balanced. For example, in the SDSS dataset, we have three times as many galaxies as quasars. To correct for this bias, we might want to select a balanced training and test set. This is achieved by balanced_train_test_split().