Merge pull request #99868 from trevorbye/master

predict_proba documentation

Author: PRMerger19

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

@@ -487,6 +487,19 @@ LogisticRegression
 'warm_start': False}
 ```
 
+### Predict class probability
+
+Models produced using automated ML all have wrapper objects that mirror functionality from their open-source origin class. Most classification model wrapper objects returned by automated ML implement the `predict_proba()` function, which accepts an array-like or sparse matrix data sample of your features (X values), and returns an n-dimensional array of each sample and it's respective class probability.
+
+Assuming you have retrieved the best run and fitted model using the same calls from above, you can call `predict_proba()` directly from the fitted model, supplying an `X_test` sample in the appropriate format depending on the model type.
+
+```python
+best_run, fitted_model = automl_run.get_output()
+class_prob = fitted_model.predict_proba(X_test)
+```
+
+If the underlying model does not support the `predict_proba()` function or the format is incorrect, a model class-specific exception will be thrown. See the [RandomForestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier.predict_proba) and [XGBoost](https://xgboost.readthedocs.io/en/latest/python/python_api.html) reference docs for examples of how this function is implemented for different model types.
+
 <a name="explain"></a>
 
 ## Model interpretability