Table 2.

The implementation of feature selection and feature dimension reduction.

Method	Implementation in Python 3.6
Lasso	LassoCV (alphas = alphas, cv = 10, max_iter = 100,000, normalize = False). fit (features, targets)
PCA	sklearn.decomposition.PCA (svd_solver = ‘auto’, n_components = num_fea)
ICA	sklearn.decomposition.FastICA (n_components = num_fea, random_state = 12, max_iter = 1,000,000)
tSNE	sklearn.manifold.TSNE (n_components = num_fea, init = ‘pca’, random_state = 12, method = ‘exact’)
UMAP	umap.UMAP (n_neighbors = 5, min_dist = 0.3, n_components = num_fea). fit_transform(features)
ISOMAP	sklearn.manifold.Isomap (n_neighbors = 5, n_components = num_fea, n_jobs = −1). fit_transform(features)