fix

Author: leemthompo

reference/data-analysis/index.md

@@ -17,9 +17,9 @@ Use high-sided functions if you want to monitor unusually high event rates. Use
 
 The {{ml-features}} include the following count functions:
 
-* [`count`, `high_count`, `low_count`](../../machine-learning/ml-count-functions.md#ml-count)
-* [`non_zero_count`, `high_non_zero_count`, `low_non_zero_count`](../../machine-learning/ml-count-functions.md#ml-nonzero-count)
-* [`distinct_count`, `high_distinct_count`, `low_distinct_count`](../../machine-learning/ml-count-functions.md#ml-distinct-count)
+* [`count`, `high_count`, `low_count`](/reference/machine-learning/ml-count-functions.md#ml-count)
+* [`non_zero_count`, `high_non_zero_count`, `low_non_zero_count`](/reference/machine-learning/ml-count-functions.md#ml-nonzero-count)
+* [`distinct_count`, `high_distinct_count`, `low_distinct_count`](/reference/machine-learning/ml-count-functions.md#ml-distinct-count)
 
 
 ## Count, high_count, low_count [ml-count]

reference/data-analysis/toc.yml

@@ -11,12 +11,12 @@ The metric functions include functions such as mean, min and max. These values a
 
 The {{ml-features}} include the following metric functions:
 
-* [`min`](../../machine-learning/ml-metric-functions.md#ml-metric-min)
-* [`max`](../../machine-learning/ml-metric-functions.md#ml-metric-max)
-* [`median`, `high_median`, `low_median`](../../machine-learning/ml-metric-functions.md#ml-metric-median)
-* [`mean`, `high_mean`, `low_mean`](../../machine-learning/ml-metric-functions.md#ml-metric-mean)
-* [`metric`](../../machine-learning/ml-metric-functions.md#ml-metric-metric)
-* [`varp`, `high_varp`, `low_varp`](../../machine-learning/ml-metric-functions.md#ml-metric-varp)
+* [`min`](/reference/machine-learning/ml-metric-functions.md#ml-metric-min)
+* [`max`](/reference/machine-learning/ml-metric-functions.md#ml-metric-max)
+* [`median`, `high_median`, `low_median`](/reference/machine-learning/ml-metric-functions.md#ml-metric-median)
+* [`mean`, `high_mean`, `low_mean`](/reference/machine-learning/ml-metric-functions.md#ml-metric-mean)
+* [`metric`](/reference/machine-learning/ml-metric-functions.md#ml-metric-metric)
+* [`varp`, `high_varp`, `low_varp`](/reference/machine-learning/ml-metric-functions.md#ml-metric-varp)
 
 ::::{note}
 You cannot add rules with conditions to detectors that use the `metric` function.

reference/machine-learning/ml-count-functions.md

@@ -23,8 +23,8 @@ The `rare` analysis detects anomalies according to the number of distinct rare v
 
 The {{ml-features}} include the following rare functions:
 
-* [`rare`](../../machine-learning/ml-rare-functions.md#ml-rare)
-* [`freq_rare`](../../machine-learning/ml-rare-functions.md#ml-freq-rare)
+* [`rare`](/reference/machine-learning/ml-rare-functions.md#ml-rare)
+* [`freq_rare`](/reference/machine-learning/ml-rare-functions.md#ml-freq-rare)
 
 
 ## Rare [ml-rare]

reference/machine-learning/ml-metric-functions.md

@@ -17,8 +17,8 @@ If your data is sparse, use `non_null_sum` functions. Buckets without values are
 
 The {{ml-features}} include the following sum functions:
 
-* [`sum`, `high_sum`, `low_sum`](../../machine-learning/ml-sum-functions.md#ml-sum)
-* [`non_null_sum`, `high_non_null_sum`, `low_non_null_sum`](../../machine-learning/ml-sum-functions.md#ml-nonnull-sum)
+* [`sum`, `high_sum`, `low_sum`](/reference/machine-learning/ml-sum-functions.md#ml-sum)
+* [`non_null_sum`, `high_non_null_sum`, `low_non_null_sum`](/reference/machine-learning/ml-sum-functions.md#ml-nonnull-sum)
 
 
 ## Sum, high_sum, low_sum [ml-sum]

reference/machine-learning/ml-rare-functions.md

@@ -11,8 +11,8 @@ The time functions detect events that happen at unusual times, either of the day
 
 The {{ml-features}} include the following time functions:
 
-* [`time_of_day`](../../machine-learning/ml-time-functions.md#ml-time-of-day)
-* [`time_of_week`](../../machine-learning/ml-time-functions.md#ml-time-of-week)
+* [`time_of_day`](/reference/machine-learning/ml-time-functions.md#ml-time-of-day)
+* [`time_of_week`](/reference/machine-learning/ml-time-functions.md#ml-time-of-week)
 
 ::::{note}
 

reference/machine-learning/ml-sum-functions.md

@@ -27,7 +27,7 @@ For more details, see the {{dfeed}} and job definitions in [GitHub](https://gith
 
 ## Apache access logs ({{filebeat}}) [apache-access-logs-filebeat]
 
-These legacy {{anomaly-jobs}} find unusual activity in HTTP access logs. For the latest versions, install the Apache integration in {{fleet}}; see [Apache access logs](../../machine-learning/ootb-ml-jobs-apache.md#apache-access-logs).
+These legacy {{anomaly-jobs}} find unusual activity in HTTP access logs. For the latest versions, install the Apache integration in {{fleet}}; see [Apache access logs](/reference/machine-learning/ootb-ml-jobs-apache.md#apache-access-logs).
 
 For more details, see the {{dfeed}} and job definitions in [GitHub](https://github.com/elastic/kibana/tree/master/x-pack/platform/plugins/shared/ml/server/models/data_recognizer/modules/apache_ecs/ml).
 

reference/machine-learning/ml-time-functions.md

@@ -27,7 +27,7 @@ These jobs are available in {{kib}} only if data exists that matches the query s
 
 ## Nginx access logs ({{filebeat}}) [nginx-access-logs-filebeat]
 
-These legacy {{anomaly-jobs}} find unusual activity in HTTP access logs. For the latest versions, install the Nginx integration in {{fleet}}; see [Nginx access logs](../../machine-learning/ootb-ml-jobs-nginx.md#nginx-access-logs).
+These legacy {{anomaly-jobs}} find unusual activity in HTTP access logs. For the latest versions, install the Nginx integration in {{fleet}}; see [Nginx access logs](/reference/machine-learning/ootb-ml-jobs-nginx.md#nginx-access-logs).
 
 These jobs exist in {{kib}} only if data exists that matches the recognizer query specified in the [manifest file](https://github.com/elastic/kibana/blob/master/x-pack/platform/plugins/shared/ml/server/models/data_recognizer/modules/nginx_ecs/manifest.json).