A tutorial exercise using Cross-validation with an SVM on the Digits dataset. This exercise is used in the

sklearn.metrics.fbeta_score(y_true, y_pred, beta, labels=None, pos_label=1, average='binary', sample_weight=None)

class sklearn.neighbors.LSHForest(n_estimators=10, radius=1.0, n_candidates=50, n_neighbors=5, min_hash_match=4, radius_cutoff_ratio=0

Silhouette analysis can be used to study the separation distance between the resulting clusters. The silhouette

sklearn.metrics.homogeneity_score(labels_true, labels_pred)

Incremental principal component analysis (IPCA) is typically used as a replacement for principal component analysis (PCA) when the dataset to be decomposed is too large to fit

class sklearn.neighbors.BallTree BallTree for fast generalized N-point problems BallTree(X, leaf_size=40, metric=

4.1.1. Pipeline: chaining estimators

Making sure that each Feature has approximately the same scale can be a crucial preprocessing step. However, when data contains outliers,

4.8.1. Label binarization