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Author: ssalgadodev

articles/machine-learning/component-reference-v2/automl-classification.md

@@ -34,7 +34,7 @@ AutoML creates a number of pipelines in parallel that try different algorithms a
 
 1. For **classification**, you can also enable deep learning.
 
-        If deep learning is enabled, validation is limited to _train_validation split_. [Learn more about validation options](/how-to-configure-cross-validation-data-splits.md).
+If deep learning is enabled, validation is limited to _train_validation split_. [Learn more about validation options](/how-to-configure-cross-validation-data-splits.md).
 
 
 1. (Optional) View addition configuration settings: additional settings you can use to better control the training job. Otherwise, defaults are applied based on experiment selection and data. 
@@ -52,13 +52,13 @@ AutoML creates a number of pipelines in parallel that try different algorithms a
 
     1. Specify the type of validation to be used for your training job. [Learn more about cross validation](/how-to-configure-cross-validation-data-splits.md#prerequisites). 
 
-    1. Provide a test dataset (preview) to evaluate the recommended model that automated ML generates for you at the end of your experiment. When you provide test data, a test job is automatically triggered at the end of your experiment. This test job is only job on the best model that was recommended by automated ML. Learn how to get the [results of the remote test job](#view-remote-test-job-results-preview).
+    1. Provide a test dataset (preview) to evaluate the recommended model that automated ML generates for you at the end of your experiment. When you provide test data, a test job is automatically triggered at the end of your experiment. This test job is only job on the best model that was recommended by automated ML. 
 
         >[!IMPORTANT]
         > Providing a test dataset to evaluate generated models is a preview feature. This capability is an [experimental](/python/api/overview/azure/ml/#stable-vs-experimental) preview feature, and may change at any time.
         
         * Test data is considered a separate from training and validation, so as to not bias the results of the test job of the recommended model. [Learn more about bias during model validation](/concept-automated-ml.md#training-validation-and-test-data).
-        * You can either provide your own test dataset or opt to use a percentage of your training dataset. Test data must be in the form of an [Azure Machine Learning TabularDataset](./v1/how-to-create-register-datasets.md#tabulardataset).         
+        * You can either provide your own test dataset or opt to use a percentage of your training dataset. Test data must be in the form of an [Azure Machine Learning TabularDataset](../v1/how-to-create-register-datasets.md#tabulardataset).         
         * The schema of the test dataset should match the training dataset. The target column is optional, but if no target column is indicated no test metrics are calculated.
         * The test dataset should not be the same as the training dataset or the validation dataset.
 

articles/machine-learning/component-reference-v2/automl-forecasting.md

@@ -53,7 +53,7 @@ AutoML creates a number of pipelines in parallel that try different algorithms a
     ------|------
     Primary metric| Main metric used for scoring your model. [Learn more about model metrics](/how-to-configure-auto-train.md#primary-metric).
     Explain best model | Select to enable or disable, in order to show explanations for the recommended best model. <br> This functionality is not currently available for [certain forecasting algorithms](/how-to-machine-learning-interpretability-automl.md#interpretability-during-training-for-the-best-model). 
-    Blocked algorithm| Select algorithms you want to exclude from the training job. <br><br> Allowing algorithms is only available for [SDK experiments](how-to-configure-auto-train.md#supported-algorithms). <br> See the [supported algorithms for each task type](/python/api/azureml-automl-core/azureml.automl.core.shared.constants.supportedmodels).
+    Blocked algorithm| Select algorithms you want to exclude from the training job. <br><br> Allowing algorithms is only available for [SDK experiments](../how-to-configure-auto-train.md#supported-algorithms). <br> See the [supported algorithms for each task type](/python/api/azureml-automl-core/azureml.automl.core.shared.constants.supportedmodels).
     Exit criterion| When any of these criteria are met, the training job is stopped. <br> *Training job time (hours)*: How long to allow the training job to run. <br> *Metric score threshold*:  Minimum metric score for all pipelines. This ensures that if you have a defined target metric you want to reach, you do not spend more time on the training job than necessary.
     Concurrency| *Max concurrent iterations*: Maximum number of pipelines (iterations) to test in the training job. The job will not run more than the specified number of iterations. Learn more about how automated ML performs [multiple child jobs on clusters](/how-to-configure-auto-train.md#multiple-child-runs-on-clusters).
 

articles/machine-learning/component-reference-v2/automl-regression.md

@@ -37,8 +37,8 @@ AutoML creates a number of pipelines in parallel that try different algorithms a
     Additional configurations|Description
     ------|------
     Primary metric| Main metric used for scoring your model. [Learn more about model metrics](..//how-to-configure-auto-train.md#primary-metric).
-    Explain best model | Select to enable or disable, in order to show explanations for the recommended best model. <br> This functionality is not currently available for [certain forecasting algorithms](how-to-machine-learning-interpretability-automl.md#interpretability-during-training-for-the-best-model). 
-    Blocked algorithm| Select algorithms you want to exclude from the training job. <br><br> Allowing algorithms is only available for [SDK experiments](how-to-configure-auto-train.md#supported-algorithms). <br> See the [supported algorithms for each task type](/python/api/azureml-automl-core/azureml.automl.core.shared.constants.supportedmodels).
+    Explain best model | Select to enable or disable, in order to show explanations for the recommended best model. <br> This functionality is not currently available for [certain forecasting algorithms](../how-to-machine-learning-interpretability-automl.md#interpretability-during-training-for-the-best-model). 
+    Blocked algorithm| Select algorithms you want to exclude from the training job. <br><br> Allowing algorithms is only available for [SDK experiments](../how-to-configure-auto-train.md#supported-algorithms). <br> See the [supported algorithms for each task type](/python/api/azureml-automl-core/azureml.automl.core.shared.constants.supportedmodels).
     Exit criterion| When any of these criteria are met, the training job is stopped. <br> *Training job time (hours)*: How long to allow the training job to run. <br> *Metric score threshold*:  Minimum metric score for all pipelines. This ensures that if you have a defined target metric you want to reach, you do not spend more time on the training job than necessary.
     Concurrency| *Max concurrent iterations*: Maximum number of pipelines (iterations) to test in the training job. The job will not run more than the specified number of iterations. Learn more about how automated ML performs [multiple child jobs on clusters](/how-to-configure-auto-train.md#multiple-child-runs-on-clusters).
 
@@ -49,18 +49,17 @@ AutoML creates a number of pipelines in parallel that try different algorithms a
     1. Specify the type of validation to be used for your training job. [Learn more about cross validation](/how-to-configure-cross-validation-data-splits.md#prerequisites). 
 
 
-    1. Provide a test dataset (preview) to evaluate the recommended model that automated ML generates for you at the end of your experiment. When you provide test data, a test job is automatically triggered at the end of your experiment. This test job is only job on the best model that was recommended by automated ML. Learn how to get the [results of the remote test job](#view-remote-test-job-results-preview).
+    1. Provide a test dataset (preview) to evaluate the recommended model that automated ML generates for you at the end of your experiment. When you provide test data, a test job is automatically triggered at the end of your experiment. This test job is only job on the best model that was recommended by automated ML.
 
         >[!IMPORTANT]
         > Providing a test dataset to evaluate generated models is a preview feature. This capability is an [experimental](/python/api/overview/azure/ml/#stable-vs-experimental) preview feature, and may change at any time.
         
-        * Test data is considered a separate from training and validation, so as to not bias the results of the test job of the recommended model. [Learn more about bias during model validation](concept-automated-ml.md#training-validation-and-test-data).
-        * You can either provide your own test dataset or opt to use a percentage of your training dataset. Test data must be in the form of an [Azure Machine Learning TabularDataset](./v1/how-to-create-register-datasets.md#tabulardataset).         
+        * Test data is considered a separate from training and validation, so as to not bias the results of the test job of the recommended model. [Learn more about bias during model validation](../concept-automated-ml.md#training-validation-and-test-data).
+        * You can either provide your own test dataset or opt to use a percentage of your training dataset. Test data must be in the form of an [Azure Machine Learning TabularDataset](../v1/how-to-create-register-datasets.md#tabulardataset).         
         * The schema of the test dataset should match the training dataset. The target column is optional, but if no target column is indicated no test metrics are calculated.
         * The test dataset should not be the same as the training dataset or the validation dataset.
         * Forecasting jobs do not support train/test split.
 
-        ![Screenshot shows the form where to select validation data and test data](media/how-to-use-automated-ml-for-ml-models/validate-test-form.png)
 
 
 

articles/machine-learning/component-reference-v2/component-reference-v2.md

@@ -1,16 +1,17 @@
 ---
-title:  "Algorithm & component reference (V2)"
-description: Learn about the Azure Machine Learning designer components that you can use to create your own machine learning projects. (V2)
+title:  "Algorithm & component reference (v2)"
+description: Learn about the Azure Machine Learning designer components that you can use to create your own machine learning projects. (v2)
 titleSuffix: Azure Machine Learning
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
 ms.topic: reference
-
-
+ms.author: shoja
+author: shouryaj
+ms.reviewer: ssalgadodev
 ms.date: 01/17/2023
 ---
-# Algorithm & component reference for Azure Machine Learning designer (V2)
+# Algorithm & component reference for Azure Machine Learning designer (v2)
 
 Azure Machine Learning designer components (Designer) allows users to create machine learning projects using a drag and drop interface. Follow this link to reach the Designer studio. Follow this link to [learn more about Designer.] (..//concept-designer)
 
@@ -45,4 +46,4 @@ For help with choosing algorithms, see
 
 ## Next steps
 
-* [Tutorial: Build a model in designer to predict auto prices](../../tutorial-designer-automobile-price-train-score.md)
+* [Tutorial: Build a model in designer to predict auto prices](../tutorial-designer-automobile-price-train-score.md)

articles/machine-learning/component-reference-v2/toc.yml

@@ -1,4 +1,4 @@
-- name: Designer algorithms & components (V2)
+- name: Designer algorithms & components (v2)
   href: component-reference-v2.md
 - name: Switch to Designer v1 documentation
   href: ../component-reference/component-reference.md