fixed warnings

Author: ssalgadodev

articles/machine-learning/reference-automated-ml-forecasting.md

@@ -37,7 +37,7 @@ The source JSON schema can be found at https://azuremlschemas.azureedge.net/late
 | `display_name` | string | The name of the job that user wants to display in the studio UI. It can be non-unique within the workspace. If it's omitted, Azure Machine Learning autogenerates a human-readable adjective-noun identifier for the display name. | | |
 | `experiment_name` | string | The name of the experiment.<br>Experiments are records of your ML training jobs on Azure. Experiments contain the results of your runs, along with logs, charts, and graphs. Each job's run record is organized under the corresponding experiment in the studio's "Experiments" tab. | | Name of the working directory in which it was created|
 | `environment_variables` | object | A dictionary object of environment variables to set on the process where the command is being executed. |  |  |
-| `outputs` | object | Represents a dictionary of output configurations of the job. The key is a name for the output within the context of the job and the value is the output configuration. See [job output](#job-output) to find out properties of this object.|  |  |  
+| `outputs` | object | Represents a dictionary of output configurations of the job. The key is a name for the output within the context of the job and the value is the output configuration. See [job output](#job-outputs) to find out properties of this object.|  |  |  
 | `log_files` | object | A dictionary object containing logs of AutoML job execution | | |
 | `log_verbosity` | string | The level of log verbosity for writing to the log file.<br>The acceptable values are defined in the Python [logging library](https://docs.python.org/3/library/logging.html).| `'not_set'`, `'debug'`, `'info'`, `'warning'`, `'error'`, `'critical'` | `'info'` |
 | `type` | const | **Required.** <br>The type of job. | `automl` | `automl` |
@@ -49,8 +49,8 @@ The source JSON schema can be found at https://azuremlschemas.azureedge.net/late
 | `primary_metric` | string | A metric that Automated ML optimizes for Time Series Forecasting model selection.<br>If `allowed_training_algorithms` has 'tcn_forecaster' to use for training, then Automated ML only supports  in 'normalized_root_mean_squared_error' and 'normalized_mean_absolute_error' to be used as primary_metric.| `"spearman_correlation"`, `"normalized_root_mean_squared_error"`, `"r2_score"` `"normalized_mean_absolute_error"`| `"normalized_root_mean_squared_error"` |
 | `training` | object | A dictionary object defining the configuration that is used in model training. <br> Check [training](#training) to find out the properties of this object.|  |  |
 | `training_data` | object | **Required**<br>A dictionary object containing the MLTable configuration defining training data to be used in as input for model training. This data is a subset of data and should be composed of both independent features/columns and target feature/column. The user can use a registered MLTable in the workspace using the format ':' (e.g Input(mltable='my_mltable:1')) OR can use a local file or folder as a MLTable(e.g Input(mltable=MLTable(local_path="./data")). This object must be provided. If target feature isn't present in source file, then Automated ML throws an error. Check [training or validation or test data](#training-or-validation-or-test-data) to find out the properties of this object. | | |
-| `validation_data` | object | A dictionary object containing the MLTable configuration defining validation data to be used within Automated ML experiment for cross validation. It should be composed of both independent features/columns and target feature/column if this object is provided. Samples in training data and validation data can't overlap in a fold. <br>See [training or validation or test data](##training-or-validation-or-test-data) to find out the properties of this object. In case this object isn't defined, then Automated ML uses `n_cross_validations` to split validation data from training data defined in `training_data` object.| | |
-| `test_data` | object | A dictionary object containing the MLTable configuration defining test data to be used in test run for predictions in using best model and evaluates the model using defined metrics. It should be composed of only independent features used in training data (without target feature) if this object is provided. <br> Check [training or validation or test data](##training-or-validation-or-test-data) to find out the properties of this object. If it isn't provided, then Automated ML uses other built-in methods to suggest best model to use for inferencing. | | |
+| `validation_data` | object | A dictionary object containing the MLTable configuration defining validation data to be used within Automated ML experiment for cross validation. It should be composed of both independent features/columns and target feature/column if this object is provided. Samples in training data and validation data can't overlap in a fold. <br>See [training or validation or test data](#training-or-validation-or-test-data) to find out the properties of this object. In case this object isn't defined, then Automated ML uses `n_cross_validations` to split validation data from training data defined in `training_data` object.| | |
+| `test_data` | object | A dictionary object containing the MLTable configuration defining test data to be used in test run for predictions in using best model and evaluates the model using defined metrics. It should be composed of only independent features used in training data (without target feature) if this object is provided. <br> Check [training or validation or test data](#training-or-validation-or-test-data) to find out the properties of this object. If it isn't provided, then Automated ML uses other built-in methods to suggest best model to use for inferencing. | | |
 
 
 
@@ -107,8 +107,8 @@ The source JSON schema can be found at https://azuremlschemas.azureedge.net/late
 | `blocked_training_algorithms` | list(string) | A list of Time Series Forecasting algorithms to not run as base model while model training in an experiment. If it is omitted or set to None, then all supported algorithms are used during model training. | `'auto_arima'`, `'prophet'`, `'naive'`, `'seasonal_naive'`, `'average'`, `'seasonal_average'`, `'exponential_smoothing'`, `'arimax'`,`'tcn_forecaster'`, `'elastic_net'`, `'gradient_boosting'`, `'decision_tree'`, `'knn'`, `'lasso_lars'`, `'sgd'`, `'random_forest'`, `'extreme_random_trees'`, `'light_gbm'`, `'xg_boost_regressor'` | `None` |
 | `enable_dnn_training` | boolean | A flag to turn on or off the inclusion of DNN based models to try out during model selection.| `True`, `False` | `False` |
 | `enable_model_explainability` | boolean |  Represents a flag to turn on model explainability like feature importance, of best model evaluated by Automated ML system. | `True`, `False` | `True` |
-| `enable_vote_ensemble` | boolean | A flag to enable or disable the ensembling of some base models using Voting algorithm. For more information about ensembles, see [Ensemble configuration](./how-to-configure-auto-train.md#ensemble). | `true`, `false` | `true` |
-| `enable_stack_ensemble` | boolean | A flag to enable or disable ensembling of some base models using Stacking algorithm. In forecasting tasks, this flag is turned off by default, to avoid risks of overfitting due to small training set used in fitting the meta learner. For more information about ensembles, see [Ensemble configuration](./how-to-configure-auto-train.md#ensemble). | `true`, `false` | `false` |
+| `enable_vote_ensemble` | boolean | A flag to enable or disable the ensembling of some base models using Voting algorithm. For more information about ensembles, see [Set up Auto train](./how-to-configure-auto-train.md). | `true`, `false` | `true` |
+| `enable_stack_ensemble` | boolean | A flag to enable or disable ensembling of some base models using Stacking algorithm. In forecasting tasks, this flag is turned off by default, to avoid risks of overfitting due to small training set used in fitting the meta learner. For more information about ensembles, see [Set up Auto trai](./how-to-configure-auto-train.md). | `true`, `false` | `false` |