Merge pull request #201762 from ssalgadodev/autoMLModelListUpdate

Link Change and added full list of models

Author: PRMerger5

articles/machine-learning/how-to-configure-auto-train.md

@@ -124,24 +124,8 @@ If you do not explicitly specify a `validation_data` or `n_cross_validation` par
 |Training data size| Validation technique |
 |---|-----|
 |**Larger than 20,000 rows**| Train/validation data split is applied. The default is to take 10% of the initial training data set as the validation set. In turn, that validation set is used for metrics calculation.
-|**Smaller&nbsp;than&nbsp;20,000&nbsp;rows**| Cross-validation approach is applied. The default number of folds depends on the number of rows. <br> **If the dataset is less than 1,000 rows**, 10 folds are used. <br> **If the rows are between 1,000 and 20,000**, then three folds are used.
+|**Smaller&nbsp;than&nbsp;or&nbsp;equal&nbsp;to&nbsp;20,000&nbsp;rows**| Cross-validation approach is applied. The default number of folds depends on the number of rows. <br> **If the dataset is fewer than 1,000 rows**, 10 folds are used. <br> **If the rows are equal to or between 1,000 and 20,000**, then three folds are used.
 
-### Large data 
-
-Automated ML supports a limited number of algorithms for training on large data that can successfully build models for big data on small virtual machines. Automated ML heuristics depend on properties such as data size, virtual machine memory size, experiment timeout and featurization settings to determine if these large data algorithms should be applied. [Learn more about what models are supported in automated ML](#supported-algorithms). 
-
-* For regression, [Online Gradient Descent Regressor](/python/api/nimbusml/nimbusml.linear_model.onlinegradientdescentregressor?preserve-view=true&view=nimbusml-py-latest) and
-[Fast Linear Regressor](/python/api/nimbusml/nimbusml.linear_model.fastlinearregressor?preserve-view=true&view=nimbusml-py-latest)
-
-* For classification, [Averaged Perceptron Classifier](/python/api/nimbusml/nimbusml.linear_model.averagedperceptronbinaryclassifier?preserve-view=true&view=nimbusml-py-latest) and [Linear SVM Classifier](/python/api/nimbusml/nimbusml.linear_model.linearsvmbinaryclassifier?preserve-view=true&view=nimbusml-py-latest);  where the Linear SVM classifier has both large data and small data versions.
-
-If you want to override these heuristics, apply the following settings: 
-
-Task | Setting | Notes
-|---|---|---
-Block&nbsp;data streaming algorithms | Use the `blocked_algorithms` parameter in the `set_training()` function and list the model(s) you don't want to use. | Results in either run failure or long run time
-Use&nbsp;data&nbsp;streaming&nbsp;algorithms| Use the `allowed_algorithms` parameter in the `set_training()` function and list the model(s) you want to use.| 
-Use&nbsp;data&nbsp;streaming&nbsp;algorithms <br> [(studio UI experiments)](how-to-use-automated-ml-for-ml-models.md#create-and-run-experiment)|Block all models except the big data algorithms you want to use. |
 
 ## Compute to run experiment
 
@@ -206,15 +190,33 @@ Automated machine learning tries different models and algorithms during the auto
 The task method determines the list of algorithms/models, to apply. Use the `allowed_algorithms` or `blocked_algorithms` parameters in the `set_training()` setter function to further modify iterations with the available models to include or exclude. 
 
 In the following list of links you can explore the supported algorithms per machine learning task listed below.
- 
-* Classification Algorithms (Tabular Data)
-* Regression Algorithms  (Tabular Data)
-* Time Series Forecasting Algorithms (Tabular Data)
+
+Classification | Regression | Time Series Forecasting
+|-- |-- |--
+[Logistic Regression](/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.classification#logisticregression----logisticregression-)* | [Elastic Net](/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.regression#elasticnet----elasticnet-)* | [AutoARIMA](/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting#autoarima----autoarima-)
+[Light GBM](/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.classification#lightgbmclassifier----lightgbm-)* | [Light GBM](/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.regression#lightgbmregressor----lightgbm-)* | [Prophet](/python/api/azureml-automl-core/azureml.automl.core.shared.constants.supportedmodels.forecasting#prophet----prophet-)
+[Gradient Boosting](/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.classification#gradientboosting----gradientboosting-)* | [Gradient Boosting](/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.regression#gradientboostingregressor----gradientboosting-)* | [Elastic Net](https://scikit-learn.org/stable/modules/linear_model.html#elastic-net)
+[Decision Tree](/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.classification#decisiontree----decisiontree-)* |[Decision Tree](/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.regression#decisiontreeregressor----decisiontree-)* |[Light GBM](https://lightgbm.readthedocs.io/en/latest/index.html)
+[K Nearest Neighbors](/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.classification#knearestneighborsclassifier----knn-)* |[K Nearest Neighbors](/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.regression#knearestneighborsregressor----knn-)* | K Nearest Neighbors
+[Linear SVC](/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.classification#linearsupportvectormachine----linearsvm-)* |[LARS Lasso](/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.regression#lassolars----lassolars-)* | [Decision Tree](https://scikit-learn.org/stable/modules/tree.html#regression)
+[Support Vector Classification (SVC)](/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.classification#supportvectormachine----svm-)* |[Stochastic Gradient Descent (SGD)](/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.regression#sgdregressor----sgd-)* | [Arimax](/python/api/azureml-automl-core/azureml.automl.core.shared.constants.supportedmodels.forecasting#arimax----arimax-)
+[Random Forest](/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.classification#randomforest----randomforest-)* | [Random Forest](/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.regression#randomforestregressor----randomforest-) | [LARS Lasso](https://scikit-learn.org/stable/modules/linear_model.html#lars-lasso)
+[Extremely Randomized Trees](https://scikit-learn.org/stable/modules/ensemble.html#extremely-randomized-trees)* | [Extremely Randomized Trees](https://scikit-learn.org/stable/modules/ensemble.html#extremely-randomized-trees)* | [Extremely Randomized Trees](https://scikit-learn.org/stable/modules/ensemble.html#extremely-randomized-trees)*
+[Xgboost](/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.classification#xgboostclassifier----xgboostclassifier-)* |[Xgboost](/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.regression#xgboostregressor----xgboostregressor-)* | [Random Forest](https://scikit-learn.org/stable/modules/ensemble.html#random-forests)
+[Naive Bayes](https://scikit-learn.org/stable/modules/naive_bayes.html#bernoulli-naive-bayes)* | [Xgboost](https://xgboost.readthedocs.io/en/latest/parameter.html)  | [ForecastTCN](/python/api/azureml-automl-core/azureml.automl.core.shared.constants.supportedmodels.forecasting#tcnforecaster----tcnforecaster-)
+[Stochastic Gradient Descent (SGD)](/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.classification#sgdclassifier----sgd-)* |[Stochastic Gradient Descent (SGD)](https://scikit-learn.org/stable/modules/sgd.html#regression) | [Gradient Boosting](https://scikit-learn.org/stable/modules/ensemble.html#regression)
+||| [ExponentialSmoothing](/python/api/azureml-automl-core/azureml.automl.core.shared.constants.supportedmodels.forecasting#exponentialsmoothing----exponentialsmoothing-)
+||| SeasonalNaive
+||| Average
+||| Naive
+||| SeasonalAverage
+
+
+With additional algorithms below.
+
 * [Image Classification Multi-class Algorithms](how-to-auto-train-image-models.md#supported-model-algorithms)
 * [Image Classification Multi-label Algorithms](how-to-auto-train-image-models.md#supported-model-algorithms)
 * [Image Object Detection Algorithms](how-to-auto-train-image-models.md#supported-model-algorithms)
-* [Image Instance Segmentation Algorithms](how-to-auto-train-image-models.md#supported-model-algorithms)
-* [NLP Text Classification Multi-class Algorithms](how-to-auto-train-nlp-models.md#language-settings)
 * [NLP Text Classification Multi-label Algorithms](how-to-auto-train-nlp-models.md#language-settings)
 * [NLP Text Named Entity Recognition (NER) Algorithms](how-to-auto-train-nlp-models.md#language-settings)