diff --git a/_ml-commons-plugin/algorithms.md b/_ml-commons-plugin/algorithms.md
index 8f7f1145..ebed62ff 100644
--- a/_ml-commons-plugin/algorithms.md
+++ b/_ml-commons-plugin/algorithms.md
@@ -57,9 +57,9 @@ POST /_plugins/_ml/_train/kmeans
 
 The training process supports multi-threads, but the number of threads should be less than half of the number of CPUs.
 
-## Linear Regression
+## Linear regression
 
-Linear Regression maps the linear relationship between inputs and outputs. In ML Commons, the linear regression algorithm is adopted from the public machine learning library [Tribuo](https://tribuo.org/), which offers multidimensional linear regression models. The model supports the linear optimizer in training, including popular approaches like Linear Decay, SQRT_DECAY, [ADA](http://chrome-extension//gphandlahdpffmccakmbngmbjnjiiahp/https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf), [ADAM](https://tribuo.org/learn/4.1/javadoc/org/tribuo/math/optimisers/Adam.html), and [RMS_DROP](https://tribuo.org/learn/4.1/javadoc/org/tribuo/math/optimisers/RMSProp.html). 
+Linear regression maps the linear relationship between inputs and outputs. In ML Commons, the linear regression algorithm is adopted from the public machine learning library [Tribuo](https://tribuo.org/), which offers multidimensional linear regression models. The model supports the linear optimizer in training, including popular approaches like Linear Decay, SQRT_DECAY, [ADA](http://chrome-extension//gphandlahdpffmccakmbngmbjnjiiahp/https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf), [ADAM](https://tribuo.org/learn/4.1/javadoc/org/tribuo/math/optimisers/Adam.html), and [RMS_DROP](https://tribuo.org/learn/4.1/javadoc/org/tribuo/math/optimisers/RMSProp.html). 
 
 ### Parameters
 
@@ -172,6 +172,8 @@ anomaly_score_threshold | double | The threshold of the anomaly score | 1.0
 
 #### Fit RCF
 
+All parameters are optional except `time_field`.
+
 Parameter | Type   | Description | Default Value
 :--- |:--- | :--- | :---
 number_of_trees | integer | The number of trees in the forest | 30
@@ -202,6 +204,8 @@ The Anomaly Localization algorithm finds subset level-information for aggregate
 
 ### Parameters
 
+All parameters are required except `filter_query` and `anomaly_start`.
+
 Parameter | Type   | Description | Default Value
 :--- | :--- | :--- | :---
 index_name | String | The data collection to analyze | N/A
@@ -217,7 +221,7 @@ anomaly_star | QueryBuilder | (Optional) The time after which the data will be a
 
 ### Example
 
-The following example executes Anomaly Localization against an RCA index. The API responds with 10 aggregations and gives the sum of the contribution and base values per aggregation, every
+The following example executes Anomaly Localization against an RCA index.
 
 **Request**
 
diff --git a/_ml-commons-plugin/api.md b/_ml-commons-plugin/api.md
index 759e8b92..5ae25d4d 100644
--- a/_ml-commons-plugin/api.md
+++ b/_ml-commons-plugin/api.md
@@ -232,7 +232,7 @@ The API returns the following:
 
 ## Predict
 
-ML Commons can predict new data with your trained model either from indexed data or a data frame. To use the Predict API, the model_id is required.
+ML Commons can predict new data with your trained model either from indexed data or a data frame. To use the Predict API, the `model_id` is required.
 
 ```json
 POST /_plugins/_ml/_predict/<algorithm_name>/<model_id>