From 78bcd5da71818a9617cf0957080a2e05933f3183 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Istv=C3=A1n=20Zolt=C3=A1n=20Szab=C3=B3?= Date: Wed, 27 May 2020 08:13:35 +0200 Subject: [PATCH] [DOCS] Removes outdated inference related limitation item. (#1104) --- .../dfanalytics-limitations.asciidoc | 26 ------------------- 1 file changed, 26 deletions(-) diff --git a/docs/en/stack/ml/df-analytics/dfanalytics-limitations.asciidoc b/docs/en/stack/ml/df-analytics/dfanalytics-limitations.asciidoc index 4205ad6e1..5433bfaa6 100644 --- a/docs/en/stack/ml/df-analytics/dfanalytics-limitations.asciidoc +++ b/docs/en/stack/ml/df-analytics/dfanalytics-limitations.asciidoc @@ -134,29 +134,3 @@ If a reduction in runtime is important to you, try strategies such as disabling feature importance, reducing the amount of training data (for example by decreasing the training percentage), setting <> values, or only selecting fields that are relevant for analysis. - -[float] -[[dfa-inference-multi-field]] -=== Analytics training on multi-field values may affect {infer} - -{dfanalytics-jobs-cap} dynamically select the best field when multi-field -values are included. For example, if a multi-field `foo` is included for training, -the `foo.keyword` is actually used. This poses a complication for {infer} with -the inference processor. Documents supplied to ingest pipelines are not mapped. Consequently, -only the field `foo` is present. This means that a model trained with the field `foo.keyword` -does not take the field `foo` into account. - -You can work around this limitation by using the `field_mappings` parameter in the inference processor. - -Example: -``` -{ - "inference": { - "model_id": "my_model_with_multi-fields", - "field_mappings": { - "foo": "foo.keyword" - }, - "inference_config": { "regression": {} } - } -} -```