Invention Grant
- Patent Title: Distributed random forest training with a predictor trained to balance tasks
-
Application No.: US16152578Application Date: 2018-10-05
-
Publication No.: US11625640B2Publication Date: 2023-04-11
- Inventor: Radek Starosta , Jan Brabec , Lukas Machlica
- Applicant: Cisco Technology, Inc.
- Applicant Address: US CA San Jose
- Assignee: Cisco Technology, Inc.
- Current Assignee: Cisco Technology, Inc.
- Current Assignee Address: US CA San Jose
- Agency: Behmke Innovation Group LLC
- Agent Kenneth J. Heywood; Jonathon P. Western
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06N7/00 ; G06N5/00

Abstract:
In one embodiment, a device distributes sets of training records from a training dataset for a random forest-based classifier among a plurality of workers of a computing cluster. Each worker determines whether it can perform a node split operation locally on the random forest by comparing a number of training records at the worker to a predefined threshold. The device determines, for each of the split operations, a data size and entropy measure of the training records to be used for the split operation. The device applies a machine learning-based predictor to the determined data size and entropy measure of the training records to be used for the split operation, to predict its completion time. The device coordinates the workers of the computing cluster to perform the node split operations in parallel such that the node split operations in a given batch are grouped based on their predicted completion times.
Information query