Merge pull request #228101 from pritamso/Broken-link-fix-shouryaj

[SCOPED] Broken link fix

Author: denrea

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

@@ -34,35 +34,35 @@ 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. 
 
     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).
+    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](../v1/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).
 
 
 1. The **[Optional] Validate and test** form allows you to do the following. 
 
-    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. 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. 
 
         >[!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).
+        * 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.
 
 
 ## Next steps
 
-See the [set of components available](/component-reference.md) to Azure Machine Learning. 
+See the [set of components available](../component-reference/component-reference.md) to Azure Machine Learning.

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

@@ -21,7 +21,7 @@ Use this component to create a machine learning model that is based on the AutoM
 
 ## How to configure 
 
-This component creates a forecasting model. Because forecasting is a supervised learning method, you need a *labeled dataset* that includes a label column with a value for all rows. Follow this link to get more information on [how to prepare your dataset.](/how-to-prepare-datasets-for-automl-images) The dataset will need a *labeled dataset* that includes a label column with a value for all rows.
+This component creates a forecasting model. Because forecasting is a supervised learning method, you need a *labeled dataset* that includes a label column with a value for all rows. Follow this link to get more information on [how to prepare your dataset.](../how-to-prepare-datasets-for-automl-images.md) The dataset will need a *labeled dataset* that includes a label column with a value for all rows.
 
 This model requires a training dataset. Validation and test datasets are optional. 
 
@@ -37,29 +37,26 @@ AutoML creates a number of pipelines in parallel that try different algorithms a
 
 1.  Specify the **Target Column** you want the model to output 
 
-1. On the **Task type and settings** form, select the task type: forecasting. See [supported task types](/concept-automated-ml.md#when-to-use-automl-classification-regression-forecasting-computer-vision--nlp) for more information.
+1. On the **Task type and settings** form, select the task type: forecasting. See [supported task types](../concept-automated-ml.md#when-to-use-automl-classification-regression-forecasting-computer-vision--nlp) for more information.
 
     1. For **forecasting** you can, 
 
         1. Enable deep learning.
 
         1. Select *time column*: This column contains the time data to be used.
 
-        1. Select *forecast horizon*: Indicate how many time units (minutes/hours/days/weeks/months/years) will the model be able to predict to the future. The further the model is required to predict into the future, the less accurate it becomes. [Learn more about forecasting and forecast horizon](/how-to-auto-train-forecast.md).
+        1. Select *forecast horizon*: Indicate how many time units (minutes/hours/days/weeks/months/years) will the model be able to predict to the future. The further the model is required to predict into the future, the less accurate it becomes. [Learn more about forecasting and forecast horizon](../how-to-auto-train-forecast.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. 
 
     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). 
+    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](../v1/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).
-
-
-
+    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).
 
 ## Next steps
 
-See the [set of components available](/component-reference.md) to Azure Machine Learning. 
+See the [set of components available](../component-reference/component-reference.md) to Azure Machine Learning.

articles/machine-learning/component-reference-v2/image-classification-multilabel.md

@@ -22,36 +22,30 @@ Multi-label image classification is a computer vision task where the goal is to
 
 ## How to configure 
 
-[Follow this link](/machine-learning/reference-automl-images-cli-multilabel-classification) for a full list of configurable parameters of this component.
+[Follow this link](/azure/machine-learning/reference-automl-images-cli-multilabel-classification) for a full list of configurable parameters of this component.
 
 [Follow this link](/machine-learning/reference-automl-images-cli-multilabel-classification) for a full list of configurable parameters of this component.
 
 This component creates a classification model. Because classification is a supervised learning method, you need a *labeled dataset* that includes a label column with a value for all rows.
 
-
 This model requires a training dataset. Validation and test datasets are optional. 
 
-Follow this link to get more information on [how to prepare your dataset.](/how-to-prepare-datasets-for-automl-images) The dataset will need a *labeled dataset* that includes a label column with a value for all rows.
-
+Follow this link to get more information on [how to prepare your dataset.](../how-to-prepare-datasets-for-automl-images.md) The dataset will need a *labeled dataset* that includes a label column with a value for all rows.
 
 AutoML runs a number of trials (specified in `max_trials`) in parallel (`specified in max_concurrent_trial`) that try different algorithms and parameters for your model. The service iterates through ML algorithms paired with hyperparameter selections and each trial produces a model with a training score. You are able to choose the metric you want the model to optimize for. The better the score for the chosen metric the better the model is considered to "fit" your data. You are able to define an exit criteria (termination policy) for the experiment. The exit criteria will be model with a specific training score you want AutoML to find. It will stop once it hits the exit criteria defined. This component will then output the best model that has been generated at the end of the run for your dataset. Visit this link for more information on [exit criteria (termination policy)](/how-to-auto-train-image-models#early-termination-policies).
 
-
-
 1.  Add the **AutoML Image Classification Multi-label** component to your pipeline.
 
 1.  Specify the **Target Column** you want the model to output 
 
-1. Specify the **Primary Metric** you want AutoML to use to measure your model's success. Visit this link for an [explanation on each primary metric for computer vision.](/how-to-auto-train-image-models.md#primary-metric)
-
-1. (Optional) You are able to configure algorithm settings. Visit this link for a {list of supported algorithms for computer vision.](/how-to-auto-train-image-models.md#supported-model-algorithms
+1. Specify the **Primary Metric** you want AutoML to use to measure your model's success. Visit this link for an [explanation on each primary metric for computer vision.](../how-to-auto-train-image-models.md#primary-metric)
 
-1. (Optional) To configure job limits, visit [this link for more explanation.](/how-to-auto-train-image-models.md#job-limits)
+1. (Optional) You are able to configure algorithm settings. Visit this link for a {list of supported algorithms for computer vision.](../how-to-auto-train-image-models.md#supported-model-algorithms
 
-1. (Optional) Visit this link for a [list of configurations for Sampling and Early Termination for your Job Sweep.](/how-to-auto-train-image-models.md#sampling-methods-for-the-sweep) You can also find more information on each of the policies and sampling methods. 
+1. (Optional) To configure job limits, visit [this link for more explanation.](../how-to-auto-train-image-models.md#job-limits)
 
-    
+1. (Optional) Visit this link for a [list of configurations for Sampling and Early Termination for your Job Sweep.](../how-to-auto-train-image-models.md#sampling-methods-for-the-sweep) You can also find more information on each of the policies and sampling methods. 
 
 ## Next steps
 
-See the [set of components available](/component-reference.md) to Azure Machine Learning. 
+See the [set of components available](../component-reference/component-reference.md) to Azure Machine Learning. 

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

@@ -21,12 +21,12 @@ Use this component to create a machine learning model that is based on the AutoM
 
 ## How to configure 
 
-[Follow this link](/machine-learning/reference-automl-images-cli-classification) for a full list of configurable parameters of this component.
+[Follow this link](/azure/machine-learning/reference-automl-images-cli-classification) for a full list of configurable parameters of this component.
 
 
 This model requires a training dataset. Validation and test datasets are optional. 
 
-Follow this link to get more information on [how to prepare your dataset.](/how-to-prepare-datasets-for-automl-images) The dataset will need a *labeled dataset* that includes a label column with a value for all rows.
+Follow this link to get more information on [how to prepare your dataset.](../how-to-prepare-datasets-for-automl-images.md) The dataset will need a *labeled dataset* that includes a label column with a value for all rows.
 
 
 AutoML runs a number of trials (specified in max_trials) in parallel (specified in max_concurrent_trials) that try different algorithms and parameters for your model. The service iterates through ML algorithms paired with hyperparameter selections and each trial produces a model with a training score. You are able to choose the metric you want the model to optimize for. The better the score for the chosen metric the better the model is considered to "fit" your data. You are able to define an exit criteria (termination policy) for the experiment. The exit criteria will be model with a specific training score you want AutoML to find. It will stop once it hits the exit criteria defined. This component will then output the best model that has been generated at the end of the run for your dataset.
@@ -37,16 +37,16 @@ AutoML runs a number of trials (specified in max_trials) in parallel (specified
 
 1.  Specify the **Target Column** you want the model to output 
 
-1. Specify the **Primary Metric** you want AutoML to use to measure your model's success. Visit this link for an [explanation on each primary metric for computer vision.](/how-to-auto-train-image-models.md#primary-metric)
+1. Specify the **Primary Metric** you want AutoML to use to measure your model's success. Visit this link for an [explanation on each primary metric for computer vision.](../how-to-auto-train-image-models.md#primary-metric)
 
-1. (Optional) You are able to configure algorithm settings. Visit this link for a {list of supported algorithms for computer vision.](/how-to-auto-train-image-models.md#supported-model-algorithms
+1. (Optional) You are able to configure algorithm settings. Visit this link for a {list of supported algorithms for computer vision.](../how-to-auto-train-image-models.md#supported-model-algorithms
 
-1. (Optional) To configure job limits, visit [this link for more explanation.](/how-to-auto-train-image-models.md#job-limits)
+1. (Optional) To configure job limits, visit [this link for more explanation.](../how-to-auto-train-image-models.md#job-limits)
 
-1. (Optional) Visit this link for a [list of configurations for Sampling and Early Termination for your Job Sweep.](/how-to-auto-train-image-models.md#sampling-methods-for-the-sweep) You can also find more information on each of the policies and sampling methods. 
+1. (Optional) Visit this link for a [list of configurations for Sampling and Early Termination for your Job Sweep.](../how-to-auto-train-image-models.md#sampling-methods-for-the-sweep) You can also find more information on each of the policies and sampling methods. 
 
 
 
 ## Next steps
 
-See the [set of components available](/component-reference.md) to Azure Machine Learning. 
+See the [set of components available](../component-reference/component-reference.md) to Azure Machine Learning.