Machine Learning Bricks

Train an XGBoost classification model.

Performs Hierarchical Agglomerative Clustering. Merges data points bottom-up.

Performs Density-Based Spatial Clustering (DBSCAN) and computes intrinsic metrics (Silhouette, etc.).

Performs clustering using Gaussian Mixture Models (GMM). Assumes data points are generated from a mixture of Gaussian distributions.

Performs Hierarchical Density-Based Spatial Clustering (HDBSCAN) and computes intrinsic metrics.

Performs K-Means clustering. Computes Extrinsic metrics if true labels are provided (ARI, NMI, etc.), otherwise computes Intrinsic metrics (Silhouette, etc.).

Performs OPTICS clustering and computes intrinsic metrics.

Performs Spectral Clustering. Uses eigenvalues of the similarity matrix to reduce dimensionality before clustering.

Matrix layout that quantifies the joint distribution between predicted and true categorical labels.

Matrix layout that quantifies the joint distribution between predicted and true categorical labels, with each cell normalized to display the percentage contribution within the overall classification.

Converting Dataframe object (string) columns to categorical types.

Perform Principal Component Analysis and generate visualization, components, summary, and sorted feature loadings.