Common Analyses: Normalisation & Standardisation in Scikit-learn

Normalisation and standardisation are two common techniques used in data preprocessing to prepare data for machine learning algorithms. In scikit-learn, a popular machine learning library in Python, these techniques can be easily implemented using various built-in functions.

Normalisation

Normalisation is the process of scaling or transforming data to have a mean of 0 and a standard deviation of 1. This is often done to ensure that all features have a similar scale, which can improve the performance of certain machine learning algorithms that are sensitive to the scale of the input data.

In scikit-learn, you can use the Normalizer class to normalise your data. Here’s an example of how to use it:


from sklearn.preprocessing import Normalizer

normalizer = Normalizer()
X_train_normalized = normalizer.fit_transform(X_train)


Standardisation

Standardisation is another technique used to transform data by scaling it to have a mean of 0 and a standard deviation of 1. However, standardisation does not necessarily bound the data to a specific range like normalisation does.

To standardise your data in scikit-learn, you can use the StandardScaler class. Here’s an example:


from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_train_standardised = scaler.fit_transform(X_train)


Both normalisation and standardisation are important steps in data preprocessing, and choosing the right technique depends on the characteristics of your data and the machine learning algorithm you are using. Experimenting with different preprocessing techniques and evaluating their impact on your model’s performance can help you make more informed decisions when building machine learning models.