update documentation

docs/source/release_notes.rst

@@ -9,7 +9,7 @@ Release Notes
     * Changes
         * Updated ``ARIMARegressor`` to be compatible with sktime v0.22.0 and beyond :pr:`4283`
         * Updated ``graph_prediction_vs_actual_over_time()`` to be compatible with multiseries time series :pr:`4284`
-        * Updated ``excluded_model_families`` to take in list of both ``str`` and ``ModelFamily`` data types :pr:`4287`
+        * Updated ``excluded_model_families`` to take in a list of both ``str`` and ``ModelFamily`` data types :pr:`4287`
     * Documentation Changes
         * Removed erroneous warnings from Data Checks User Guide page and removed ``tqdm`` warning in all notebooks :pr:`4274`
     * Testing Changes

docs/source/user_guide/automl.ipynb

@@ -903,8 +903,7 @@
     "`DefaultAlgorithm` does this by creating the concept of two modes: `fast` and `long`, where `fast` is a subset of long. The algorithm runs as follows:\n",
     "\n",
     "1. Run naive pipelines:\n",
-    "   a. a linear model with the default preprocessing pipeline\n",
-    "   b. a random forest pipeline with the default preprocessing pipeline\n",
+    "    a. a random forest pipeline with the default preprocessing pipeline\n",
     "   \n",
     "2. Run the same pipelines, this time with feature selection. Subsequent pipelines will use the selected features with a SelectedColumns transformer.\n",
     "\n",
@@ -924,7 +923,7 @@
     "    a. For each of the previous top 3 estimators, sample 10 parameters from the tuner. Run all 30 in one batch\n",
     "    b. Run ensembling\n",
     "    \n",
-    "To this end, it is recommended to use the top level `search()` method to run `DefaultAlgorithm`. This allows users to specify running search with just the `mode` parameter, where `fast` is recommended for users who want a fast scan at how EvalML pipelines will perform on their problem and where `long` is reserved for a deeper dive into high performing pipelines. If one needs finer control over AutoML parameters, one can also specify `automl_algorithm='default'` using `AutoMLSearch` and it will default to using `fast` mode. However, in this case ensembling will be defined by the `ensembling` flag (if `ensembling=False` the abovementioned ensembling batches will be skipped). Users are welcome to select `max_batches` according to the algorithm above (or other stopping criteria) but should be aware that results may not be optimal if the algorithm does not run for the full length of `fast` mode."
+    "To this end, it is recommended to use the top level `search()` method to run `DefaultAlgorithm`. This allows users to specify running search with just the `mode` parameter, where `fast` is recommended for users who want a fast scan at how EvalML pipelines will perform on their problem and where `long` is reserved for a deeper dive into high performing pipelines. If one needs finer control over AutoML parameters, one can also specify `automl_algorithm='default'` using `AutoMLSearch` and it will default to using `fast` mode. However, in this case ensembling will be defined by the `ensembling` flag (if `ensembling=False` the abovementioned ensembling batches will be skipped). Users are welcome to select `max_batches` according to the algorithm above (or other stopping criteria) but should be aware that results may not be optimal if the algorithm does not run for the full length of `fast` mode. Note that the `allowed_model_families` and `excluded_model_families` parameters are only applied to the non-naive batches in the default algorithms. If users want to apply these to all estimators, use the iterative algorithm by specifying `automl_algorithm='iterative'`."
    ]
   },
   {
@@ -1200,7 +1199,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {