Merge pull request #110225 from nibaccam/onnx-algo

AutoML | Supported algorithms w/ ONNX

Author: PRMerger10

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

@@ -38,24 +38,27 @@ Before you begin your experiment, you should determine the kind of machine learn
 
 Automated machine learning supports the following algorithms during the automation and tuning process. As a user, there is no need for you to specify the algorithm.
 
+> [!NOTE]
+> If you plan to export your auto ML created models to an [ONNX model](concept-onnx.md), only those algorithms indicated with an * are able to be converted to the ONNX format. Learn more about [converting models to ONNX](concept-automated-ml.md#use-with-onnx-in-c-apps). <br> <br> Also note, ONNX only supports classification and regression tasks at this time. 
+
 Classification | Regression | Time Series Forecasting
 |-- |-- |--
-[Logistic Regression](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression)| [Elastic Net](https://scikit-learn.org/stable/modules/linear_model.html#elastic-net)| [Elastic Net](https://scikit-learn.org/stable/modules/linear_model.html#elastic-net)
-[Light GBM](https://lightgbm.readthedocs.io/en/latest/index.html)|[Light GBM](https://lightgbm.readthedocs.io/en/latest/index.html)|[Light GBM](https://lightgbm.readthedocs.io/en/latest/index.html)
-[Gradient Boosting](https://scikit-learn.org/stable/modules/ensemble.html#classification)|[Gradient Boosting](https://scikit-learn.org/stable/modules/ensemble.html#regression)|[Gradient Boosting](https://scikit-learn.org/stable/modules/ensemble.html#regression)
-[Decision Tree](https://scikit-learn.org/stable/modules/tree.html#decision-trees)|[Decision Tree](https://scikit-learn.org/stable/modules/tree.html#regression)|[Decision Tree](https://scikit-learn.org/stable/modules/tree.html#regression)
-[K Nearest Neighbors](https://scikit-learn.org/stable/modules/neighbors.html#nearest-neighbors-regression)|[K Nearest Neighbors](https://scikit-learn.org/stable/modules/neighbors.html#nearest-neighbors-regression)|[K Nearest Neighbors](https://scikit-learn.org/stable/modules/neighbors.html#nearest-neighbors-regression)
-[Linear SVC](https://scikit-learn.org/stable/modules/svm.html#classification)|[LARS Lasso](https://scikit-learn.org/stable/modules/linear_model.html#lars-lasso)|[LARS Lasso](https://scikit-learn.org/stable/modules/linear_model.html#lars-lasso)
-[Support Vector Classification (SVC)](https://scikit-learn.org/stable/modules/svm.html#classification)|[Stochastic Gradient Descent (SGD)](https://scikit-learn.org/stable/modules/sgd.html#regression)|[Stochastic Gradient Descent (SGD)](https://scikit-learn.org/stable/modules/sgd.html#regression)
-[Random Forest](https://scikit-learn.org/stable/modules/ensemble.html#random-forests)|[Random Forest](https://scikit-learn.org/stable/modules/ensemble.html#random-forests)|[Random Forest](https://scikit-learn.org/stable/modules/ensemble.html#random-forests)
-[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](https://xgboost.readthedocs.io/en/latest/parameter.html)|[Xgboost](https://xgboost.readthedocs.io/en/latest/parameter.html)| [Xgboost](https://xgboost.readthedocs.io/en/latest/parameter.html)
-[DNN Classifier](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier)|[DNN Regressor](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNRegressor) | [DNN Regressor](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNRegressor)|
-[DNN Linear Classifier](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearClassifier)|[Linear Regressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor)|[Linear Regressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor)
-[Naive Bayes](https://scikit-learn.org/stable/modules/naive_bayes.html#bernoulli-naive-bayes)|[Fast Linear Regressor](https://docs.microsoft.com/python/api/nimbusml/nimbusml.linear_model.fastlinearregressor?view=nimbusml-py-latest)|[Auto-ARIMA](https://www.alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.auto_arima.html#pmdarima.arima.auto_arima)
-[Stochastic Gradient Descent (SGD)](https://scikit-learn.org/stable/modules/sgd.html#sgd)|[Online Gradient Descent Regressor](https://docs.microsoft.com/python/api/nimbusml/nimbusml.linear_model.onlinegradientdescentregressor?view=nimbusml-py-latest)|[Prophet](https://facebook.github.io/prophet/docs/quick_start.html)
+[Logistic Regression](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression)* | [Elastic Net](https://scikit-learn.org/stable/modules/linear_model.html#elastic-net)* | [Elastic Net](https://scikit-learn.org/stable/modules/linear_model.html#elastic-net)
+[Light GBM](https://lightgbm.readthedocs.io/en/latest/index.html)* |[Light GBM](https://lightgbm.readthedocs.io/en/latest/index.html)*|[Light GBM](https://lightgbm.readthedocs.io/en/latest/index.html)
+[Gradient Boosting](https://scikit-learn.org/stable/modules/ensemble.html#classification)* |[Gradient Boosting](https://scikit-learn.org/stable/modules/ensemble.html#regression)* |[Gradient Boosting](https://scikit-learn.org/stable/modules/ensemble.html#regression)
+[Decision Tree](https://scikit-learn.org/stable/modules/tree.html#decision-trees)* |[Decision Tree](https://scikit-learn.org/stable/modules/tree.html#regression)* |[Decision Tree](https://scikit-learn.org/stable/modules/tree.html#regression)
+[K Nearest Neighbors](https://scikit-learn.org/stable/modules/neighbors.html#nearest-neighbors-regression)* |[K Nearest Neighbors](https://scikit-learn.org/stable/modules/neighbors.html#nearest-neighbors-regression)* |[K Nearest Neighbors](https://scikit-learn.org/stable/modules/neighbors.html#nearest-neighbors-regression)
+[Linear SVC](https://scikit-learn.org/stable/modules/svm.html#classification)* |[LARS Lasso](https://scikit-learn.org/stable/modules/linear_model.html#lars-lasso)* |[LARS Lasso](https://scikit-learn.org/stable/modules/linear_model.html#lars-lasso)
+[Support Vector Classification (SVC)](https://scikit-learn.org/stable/modules/svm.html#classification)* |[Stochastic Gradient Descent (SGD)](https://scikit-learn.org/stable/modules/sgd.html#regression)* |[Stochastic Gradient Descent (SGD)](https://scikit-learn.org/stable/modules/sgd.html#regression)
+[Random Forest](https://scikit-learn.org/stable/modules/ensemble.html#random-forests)* |[Random Forest](https://scikit-learn.org/stable/modules/ensemble.html#random-forests)* |[Random Forest](https://scikit-learn.org/stable/modules/ensemble.html#random-forests)
+[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](https://xgboost.readthedocs.io/en/latest/parameter.html)* |[Xgboost](https://xgboost.readthedocs.io/en/latest/parameter.html)* | [Xgboost](https://xgboost.readthedocs.io/en/latest/parameter.html)
+[DNN Classifier](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier) |[DNN Regressor](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNRegressor) | [DNN Regressor](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNRegressor)|
+[DNN Linear Classifier](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearClassifier)|[Linear Regressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor) |[Linear Regressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor)
+[Naive Bayes](https://scikit-learn.org/stable/modules/naive_bayes.html#bernoulli-naive-bayes)* |[Fast Linear Regressor](https://docs.microsoft.com/python/api/nimbusml/nimbusml.linear_model.fastlinearregressor?view=nimbusml-py-latest)|[Auto-ARIMA](https://www.alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.auto_arima.html#pmdarima.arima.auto_arima)
+[Stochastic Gradient Descent (SGD)](https://scikit-learn.org/stable/modules/sgd.html#sgd)* |[Online Gradient Descent Regressor](https://docs.microsoft.com/python/api/nimbusml/nimbusml.linear_model.onlinegradientdescentregressor?view=nimbusml-py-latest)|[Prophet](https://facebook.github.io/prophet/docs/quick_start.html)
 |[Averaged Perceptron Classifier](https://docs.microsoft.com/python/api/nimbusml/nimbusml.linear_model.averagedperceptronbinaryclassifier?view=nimbusml-py-latest)||ForecastTCN
-|[Linear SVM Classifier](https://docs.microsoft.com/python/api/nimbusml/nimbusml.linear_model.linearsvmbinaryclassifier?view=nimbusml-py-latest)||
+|[Linear SVM Classifier](https://docs.microsoft.com/python/api/nimbusml/nimbusml.linear_model.linearsvmbinaryclassifier?view=nimbusml-py-latest)* ||
 
 Use the `task` parameter in the `AutoMLConfig` constructor to specify your experiment type.