Sklearn Introduction (page 2 of 4)

With Sklearn, we can perform (among many others) the following analyses:

Supervised Learning algorithms: Supports most of the popular supervised learning algorithms (e.g., Linear Regression, Decision Trees, K-Nearest Neighbors, and Random Forests).
Unsupervised Learning algorithms: Supports most of the popular unsupervised learning algorithms (e.g., clustering, factor analysis, PCA (Principal Component Analysis).
Cross Validation: Supports k-fold cross-validation.
Dimensionality Reduction: Supports reducing the number of features to facilitate model creation (note that this is often a component of unsupervised machine learning).
Ensemble methods: Supports bagging models to combine predictions across multiple models and other ensemble methods.
Feature extraction: Supports feature extraction from image or text data
Feature selection: Supports the identification of useful features to train supervised models.