  We use machine learning techniques to investigate their performance in
classifying active galactic nuclei (AGNs), including X-ray selected AGNs
(XAGNs), infrared selected AGNs (IRAGNs), and radio selected AGNs (RAGNs).
Using known physical parameters in the Cosmic Evolution Survey (COSMOS) field,
we are able to well-established training samples in the region of Hyper
Suprime-Cam (HSC) survey. We compare several Python packages (e.g.,
scikit-learn, Keras, and XGBoost), and use XGBoost to identify AGNs and show
the performance (e.g., accuracy, precision, recall, F1 score, and AUROC). Our
results indicate that the performance is high for bright XAGN and IRAGN host
galaxies. The combination of the HSC (optical) information with the Wide-field
Infrared Survey Explorer (WISE) band-1 and WISE band-2 (near-infrared)
information perform well to identify AGN hosts. For both type-1 (broad-line)
XAGNs and type-1 (unobscured) IRAGNs, the performance is very good by using
optical to infrared information. These results can apply to the five-band data
from the wide regions of the HSC survey, and future all-sky surveys.
