Hands-On Ensemble Learning with Python

Dimensionality reduction

Another form of unsupervised learning is dimensionality reduction. The number of features present in a dataset equals the dataset's dimensions. Often, many features can be correlated, noisy, or simply not provide much information. Nonetheless, the cost of storing and processing data is correlated with a dataset's dimensionality. Thus, by reducing the dataset's dimensions, we can help the algorithms to better model the data.

Another use of dimensionality reduction is for the visualization of high-dimensional datasets. For example, using the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm, we can reduce the breast cancer dataset to two dimensions or components. Although it is not easy to visualize 30 dimensions, ...