Hands-On Machine Learning for Algorithmic Trading

PCA with sklearn

The sklearn.decomposition.PCA implementation follows the standard API based on the fit() and transform() methods, which compute the desired number of principal components and project the data into the component space, respectively. The convenience method fit_transform() accomplishes this in a single step.

PCA offers three different algorithms that can be specified using the svd_solver parameter:

• Full computes the exact SVD using the LAPACK solver provided by SciPy
• Arpack runs a truncated version suitable for computing less than the full number of components
• Randomized uses a sampling-based algorithm that is more efficient when the dataset has more than 500 observations and features, and the goal is to compute less than ...