Dimensionality reduction

Preprocessing for Machine Learning in Python

James Chapman

Curriculum Manager, DataCamp

Dimensionality reduction and PCA

Unsupervised learning method
Combines/decomposes a feature space
Feature extraction - here we'll use to reduce our feature space

Principal component analysis
Linear transformation to uncorrelated space
Captures as much variance as possible in each component

PCA in scikit-learn

from sklearn.decomposition import PCA
pca = PCA()
df_pca = pca.fit_transform(df)


print(df_pca)

[88.4583, 18.7764, -2.2379, ..., 0.0954, 0.0361, -0.0034],
[93.4564, 18.6709, -1.7887, ..., -0.0509, 0.1331, 0.0119],
[-186.9433, -0.2133, -5.6307, ..., 0.0332, 0.0271, 0.0055]

print(pca.explained_variance_ratio_)

[0.9981, 0.0017, 0.0001, 0.0001, ...]

PCA caveats

Difficult to interpret components
End of preprocessing journey

Let's practice!

Preprocessing for Machine Learning in Python