From 03733c2f81338c01a2e7947e947d932cc4d5e94a Mon Sep 17 00:00:00 2001
From: Brad Quarry <38725582+bradquarry@users.noreply.github.com>
Date: Tue, 18 Nov 2025 11:58:14 -0500
Subject: [PATCH] Refine description of anomaly detection techniques

"use a bespoke amalgamation of techniques"

 This phrase is very typically interpreted as "a bunch of popsicle stick and chewing gum features" when customers read it and this is not the case. I suggest this simple change to clear up this perception.

I suggest replacing this with "use a combination of advanced mathematical techniques"
---
 .../machine-learning/anomaly-detection/ml-ad-algorithms.md      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/explore-analyze/machine-learning/anomaly-detection/ml-ad-algorithms.md b/explore-analyze/machine-learning/anomaly-detection/ml-ad-algorithms.md
index f9a0d9b9af..10ea9126b8 100644
--- a/explore-analyze/machine-learning/anomaly-detection/ml-ad-algorithms.md
+++ b/explore-analyze/machine-learning/anomaly-detection/ml-ad-algorithms.md
@@ -10,7 +10,7 @@ products:
 
 # Anomaly detection algorithms [ml-ad-algorithms]
 
-The {{anomaly-detect}} {{ml-features}} use a bespoke amalgamation of different techniques such as clustering, various types of time series decomposition, Bayesian distribution modeling, and correlation analysis. These analytics provide sophisticated real-time automated {{anomaly-detect}} for time series data.
+The {{anomaly-detect}} {{ml-features}} use a combination of advanced mathematical techniques such as clustering, various types of time series decomposition, Bayesian distribution modeling, and correlation analysis. These analytics provide sophisticated real-time automated {{anomaly-detect}} for time series data.
 
 The {{ml}} analytics statistically model the time-based characteristics of your data by observing historical behavior and adapting to new data. The model represents a baseline of normal behavior and can therefore be used to determine how anomalous new events are.