[DOCS] Synch or remove shared attributes (#2367)

Author: lcawl

docs/en/getting-started/index.asciidoc

@@ -1,16 +1,6 @@
 = Getting Started
 
 :doctype: book
-
-:forum: https://discuss.elastic.co/c/x-pack
-:security-forum: https://discuss.elastic.co/c/shield
-:watcher-forum: https://discuss.elastic.co/c/watcher
-:monitoring-forum: https://discuss.elastic.co/c/marvel
-:graph-forum: https://discuss.elastic.co/c/graph
-
-:blog-ref: https://www.elastic.co/blog/
-:wikipedia: https://en.wikipedia.org/wiki
-
 :kib-repo-dir: {kibana-root}/docs
 :xes-repo-dir: {elasticsearch-root}/x-pack/docs/en
 :es-repo-dir: {elasticsearch-root}/docs/reference

docs/en/stack/ml/anomaly-detection/anomaly-how-tos.asciidoc

@@ -28,16 +28,16 @@ The guides in this section describe some best practices for generating useful
 The blog posts listed below show how to get the most out of Elastic {ml} 
 {anomaly-detect}.
 
-* https://www.elastic.co/blog/sizing-machine-learning-with-elasticsearch[Sizing for {ml} with {es}]
-* https://www.elastic.co/blog/filtering-input-data-to-refine-machine-learning-jobs[Filtering input data to refine {ml-jobs}]
-* https://www.elastic.co/blog/temporal-vs-population-analysis-in-elastic-machine-learning[Temporal vs. population analysis in Elastic {ml}]
-* https://www.elastic.co/blog/using-elasticsearch-and-machine-learning-for-it-operations[Using {es} and {ml} for IT Operations]
-* https://www.elastic.co/blog/using-machine-learning-and-elasticsearch-for-security-analytics-deep-dive[Using {ml} and {es} for security analytics]
-* https://www.elastic.co/blog/augmenting-results-with-user-annotations-for-elastic-machine-learning[User annotations for Elastic {ml}]
-* https://www.elastic.co/blog/custom-elasticsearch-aggregations-for-machine-learning-jobs[Custom {es} aggregations for {ml-jobs}]
-* https://www.elastic.co/blog/analysing-linux-auditd-anomalies-with-auditbeat-and-elastic-stack-machine-learning[Analysing Linux auditd anomalies with Auditbeat and {ml}]
-* https://www.elastic.co/blog/how-to-optimize-elasticsearch-machine-learning-job-configurations-using-job-validation[How to optimize {es} {ml} job configurations using job validation]
-* https://www.elastic.co/blog/interpretability-in-ml-identifying-anomalies-influencers-root-causes[Interpretability in {ml}: Identifying anomalies, influencers, and root causes]
+* {blog-ref}sizing-machine-learning-with-elasticsearch[Sizing for {ml} with {es}]
+* {blog-ref}filtering-input-data-to-refine-machine-learning-jobs[Filtering input data to refine {ml-jobs}]
+* {blog-ref}temporal-vs-population-analysis-in-elastic-machine-learning[Temporal vs. population analysis in Elastic {ml}]
+* {blog-ref}using-elasticsearch-and-machine-learning-for-it-operations[Using {es} and {ml} for IT Operations]
+* {blog-ref}using-machine-learning-and-elasticsearch-for-security-analytics-deep-dive[Using {ml} and {es} for security analytics]
+* {blog-ref}augmenting-results-with-user-annotations-for-elastic-machine-learning[User annotations for Elastic {ml}]
+* {blog-ref}custom-elasticsearch-aggregations-for-machine-learning-jobs[Custom {es} aggregations for {ml-jobs}]
+* {blog-ref}analysing-linux-auditd-anomalies-with-auditbeat-and-elastic-stack-machine-learning[Analysing Linux auditd anomalies with Auditbeat and {ml}]
+* {blog-ref}how-to-optimize-elasticsearch-machine-learning-job-configurations-using-job-validation[How to optimize {es} {ml} job configurations using job validation]
+* {blog-ref}interpretability-in-ml-identifying-anomalies-influencers-root-causes[Interpretability in {ml}: Identifying anomalies, influencers, and root causes]
 
 There are also some examples in the {ml} folder in the https://github.com/elastic/examples[examples repository].
 

docs/en/stack/ml/anomaly-detection/ml-influencers.asciidoc

@@ -46,7 +46,7 @@ more than three. If you pick many influencers, the results can be overwhelming
 and there is a small overhead to the analysis.
 
 Refer to 
-https://www.elastic.co/blog/interpretability-in-ml-identifying-anomalies-influencers-root-causes[this blog post]
+{blog-ref}interpretability-in-ml-identifying-anomalies-influencers-root-causes[this blog post]
 for further details on influencers.
 
 

docs/en/stack/ml/anomaly-detection/ml-limitations.asciidoc

@@ -29,7 +29,7 @@ the Elastic {ml-features}. The limitations are grouped into four categories:
 
 {ml-cap} uses Streaming SIMD Extensions (SSE) 4.2 instructions, so it works only
 on machines whose CPUs 
-https://en.wikipedia.org/wiki/SSE4#Supporting_CPUs[support] SSE4.2. If you run 
+{wikipedia}/SSE4#Supporting_CPUs[support] SSE4.2. If you run 
 {es} on older hardware you must disable {ml} by setting `xpack.ml.enabled` to 
 `false`. See {ref}/ml-settings.html[{ml-cap} settings in {es}].
 

docs/en/stack/ml/df-analytics/ml-dfa-classification.asciidoc

@@ -1048,8 +1048,8 @@ destination indices and {data-sources}.
 === Further readings
 
 * https://github.com/elastic/examples/tree/master/Machine%20Learning/Analytics%20Jupyter%20Notebooks[{classanalysis-cap} example (Jupyter notebook)]
-* https://www.elastic.co/blog/benchmarking-binary-classification-results-in-elastic-machine-learning[Benchmarking binary {classification} results in Elastic {ml}]
-* https://www.elastic.co/blog/using-elastic-supervised-machine-learning-for-binary-classification[Using Elastic supervised {ml} for binary {classification}]
-* https://www.elastic.co/blog/machine-learning-in-cybersecurity-training-supervised-models-to-detect-dga-activity[{ml-cap} in cybersecurity – part 1: Training supervised models to detect DGA activity]
-* https://www.elastic.co/blog/machine-learning-in-cybersecurity-detecting-dga-activity-in-network-data[{ml-cap} in cybersecurity – part 2: Detecting DGA activity in network data]
-* https://www.elastic.co/blog/supervised-and-unsupervised-machine-learning-for-dga-detection[Combining supervised and unsupervised machine learning for DGA detection]
+* {blog-ref}benchmarking-binary-classification-results-in-elastic-machine-learning[Benchmarking binary {classification} results in Elastic {ml}]
+* {blog-ref}using-elastic-supervised-machine-learning-for-binary-classification[Using Elastic supervised {ml} for binary {classification}]
+* {blog-ref}machine-learning-in-cybersecurity-training-supervised-models-to-detect-dga-activity[{ml-cap} in cybersecurity – part 1: Training supervised models to detect DGA activity]
+* {blog-ref}machine-learning-in-cybersecurity-detecting-dga-activity-in-network-data[{ml-cap} in cybersecurity – part 2: Detecting DGA activity in network data]
+* {blog-ref}supervised-and-unsupervised-machine-learning-for-dga-detection[Combining supervised and unsupervised machine learning for DGA detection]

docs/en/stack/ml/df-analytics/ml-dfa-outlier-detection.asciidoc

@@ -560,7 +560,7 @@ the destination indices and {data-sources}.
 * If you want to see another example of {oldetection} in a Jupyter notebook,
 https://github.com/elastic/examples/tree/master/Machine%20Learning/Outlier%20Detection/Introduction[click here].
 
-* https://www.elastic.co/blog/catching-malware-with-elastic-outlier-detection[This blog post]
+* {blog-ref}catching-malware-with-elastic-outlier-detection[This blog post]
 shows you how to catch malware using {oldetection}.
 
-* https://www.elastic.co/blog/benchmarking-outlier-detection-in-elastic-machine-learning[Benchmarking {oldetection} results in Elastic {ml}]
+* {blog-ref}benchmarking-outlier-detection-in-elastic-machine-learning[Benchmarking {oldetection} results in Elastic {ml}]

docs/en/stack/ml/df-analytics/ml-dfa-overview.asciidoc

@@ -83,7 +83,7 @@ like {anomaly-detect} or {oldetection} – does not have this requirement.
 * {regression}: predicts **continuous, numerical values** like the response time 
  of a web request. 
 * {classification}: predicts **discrete, categorical values** like whether a 
- https://www.elastic.co/blog/machine-learning-in-cybersecurity-training-supervised-models-to-detect-dga-activity[DNS request originates from a malicious or benign domain]. 
+ {blog-ref}machine-learning-in-cybersecurity-training-supervised-models-to-detect-dga-activity[DNS request originates from a malicious or benign domain]. 
 
 
 [discrete]
@@ -181,4 +181,4 @@ the model is used to infer against the data and make predictions on it.
 <<ml-dfanalytics-classification-evaluation,classification>> models.
 * Find out how to deploy your model by using {infer} for 
 <<ml-inference-class,{classification}>> and <<ml-inference-reg,{regression}>>.
-* https://www.elastic.co/blog/train-evaluate-monitor-infer-end-to-end-machine-learning-in-elastic[Train, evaluate, monitor, infer: End-to-end machine learning in Elastic].
+* {blog-ref}train-evaluate-monitor-infer-end-to-end-machine-learning-in-elastic[Train, evaluate, monitor, infer: End-to-end machine learning in Elastic].

docs/en/stack/ml/df-analytics/ml-dfa-regression-loss-functions.asciidoc

@@ -12,13 +12,13 @@ trees to compare the performance of various iterations of the model.
 
 In the {stack}, there are three different types of loss function:
 
-* https://en.wikipedia.org/wiki/Mean_squared_error[mean squared error (`mse`)]: 
+* {wikipedia}/Mean_squared_error[mean squared error (`mse`)]: 
 It is the default choice when no additional information about the data set is 
 available.
 * mean squared logarithmic error (`msle`; a variation of `mse`): It is for 
 cases where the target values are all positive with a long tail distribution 
 (for example, prices or population).
-* https://en.wikipedia.org/wiki/Huber_loss#Pseudo-Huber_loss_function[Pseudo-Huber loss (`huber`)]:
+* {wikipedia}/Huber_loss#Pseudo-Huber_loss_function[Pseudo-Huber loss (`huber`)]:
 Use it when you want to prevent the model trying to fit the outliers instead of 
 regular data.
 

docs/en/stack/ml/df-analytics/ml-dfa-regression.asciidoc

@@ -155,7 +155,7 @@ more about loss functions.
 [[ml-dfanalytics-huber]]
 === Pseudo-Huber loss
 
-https://en.wikipedia.org/wiki/Huber_loss#Pseudo-Huber_loss_function[Pseudo-Huber loss metric] 
+{wikipedia}/Huber_loss#Pseudo-Huber_loss_function[Pseudo-Huber loss metric] 
 behaves as mean absolute error (MAE) for errors larger than a predefined value 
 (defaults to `1`) and as mean squared error (MSE) for errors smaller than the 
 predefined value. This loss function uses the `delta` parameter to define the 

docs/en/stack/ml/df-analytics/ml-feature-importance.asciidoc

@@ -70,5 +70,5 @@ prediction.
 * {feat-imp-cap} in the {stack} is calculated using the SHAP (SHapley Additive 
 exPlanations) method as described in
 https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf[Lundberg, S. M., & Lee, S.-I. A Unified Approach to Interpreting Model Predictions. In NeurIPS 2017].
-* https://www.elastic.co/blog/feature-importance-for-data-frame-analytics-with-elastic-machine-learning[{feat-imp-cap} for {dfanalytics} with Elastic {ml}].
+* {blog-ref}feature-importance-for-data-frame-analytics-with-elastic-machine-learning[{feat-imp-cap} for {dfanalytics} with Elastic {ml}].
 * https://github.com/elastic/examples/tree/master/Machine%20Learning/Feature%20Importance[Feature importance for {dfanalytics} (Jupyter notebook)].