adds comment explaining hnsw vs flat index choice

Author: justin-cechmanek

python-recipes/fraud-detection/00_fraud_detection.ipynb

@@ -507,7 +507,14 @@
         "We index the raw transaction fields (so we can filter and investigate) plus the\n",
         "feature `vector`. We use an **HNSW** vector index with **L2 (Euclidean)**\n",
         "distance — for standardized feature vectors, L2 directly measures how different\n",
-        "two transactions' behaviors are."
+        "two transactions' behaviors are.\n",
+        "\n",
+        "We've chosen the **HNSW** approximate nearest neighbors algorithm instead of the\n",
+        "exact **Flat** nearest neighbors algorithm because in real applications your system\n",
+        "will likely have many more transactions than this notebook and speed is key for\n",
+        "catching fraudulent charges as they happen. The **HNSW** algorithm isn't guaranteed\n",
+        "to find the closest transactions, but it is able to scale to millions of transactions\n",
+        "while maintaining a fast response rate."
       ]
     },
     {