[DOC] Examples added to docstrings for Time Series Forest Regressor a…

…nd Dummy Regressor (#4775)

Additions to #4264 

Added examples to docstrings in _tsf.py and _dummy.py. Fixed a typo in
docstring for ResNetRegressor example.

.all-contributorsrc

@@ -2199,6 +2199,15 @@
       "contributions": [
         "doc"
       ]
+    },
+    {
+      "login": "mgazian000",
+      "name": "Michael Gaziani",
+      "avatar_url": "https://avatars.githubusercontent.com/mgazian000",
+      "profile": "https://github.com/mgazian000",
+      "contributions": [
+        "doc"
+      ]
     }
   ]
 }

sktime/regression/deep_learning/resnet.py

@@ -54,7 +54,7 @@ class ResNetRegressor(BaseDeepRegressor):
     >>> from sktime.regression.deep_learning.resnet import ResNetRegressor
     >>> from sktime.datasets import load_unit_test
     >>> X_train, y_train = load_unit_test(split="train")
-    >>> clf = ResNetRegressor(n_epochs=20, bacth_size=4) # doctest: +SKIP
+    >>> clf = ResNetRegressor(n_epochs=20, batch_size=4) # doctest: +SKIP
     >>> clf.fit(X_train, Y_train) # doctest: +SKIP
     ResNetRegressor(...)
     """

sktime/regression/dummy/_dummy.py

@@ -44,6 +44,17 @@ class DummyRegressor(BaseRegressor):
         The quantile to predict using the "quantile" strategy. A quantile of
         0.5 corresponds to the median, while 0.0 to the minimum and 1.0 to the
         maximum.
+
+    Examples
+    --------
+    >>> from sktime.regression.dummy import DummyRegressor
+    >>> from sktime.datasets import load_unit_test
+    >>> X_train, y_train = load_unit_test(split="train")
+    >>> X_test, y_test = load_unit_test(split="test")
+    >>> regressor = DummyRegressor(strategy="median") # doctest: +SKIP
+    >>> regressor.fit(X_train,y_train) # doctest: +SKIP
+    DummyRegressor(strategy='median')
+    >>> y_pred = regressor.predict(X_test) # doctest: +SKIP
     """
 
     _tags = {

sktime/regression/interval_based/_tsf.py

@@ -63,6 +63,17 @@ class TimeSeriesForestRegressor(BaseTimeSeriesForest, ForestRegressor, BaseRegre
        classification and feature extraction", Information Sciences, 239, 2013
     .. [2] Java implementation https://github.com/uea-machine-learning/tsml
     .. [3] Arxiv paper: https://arxiv.org/abs/1302.2277
+
+    Examples
+    --------
+    >>> from sktime.regression.interval_based import TimeSeriesForestRegressor
+    >>> from sktime.datasets import load_unit_test
+    >>> X_train, y_train = load_unit_test(split="train")
+    >>> X_test, y_test = load_unit_test(split="test")
+    >>> regressor = TimeSeriesForestRegressor(n_estimators=150) # doctest: +SKIP
+    >>> regressor.fit(X_train, y_train) # doctest: +SKIP
+    TimeSeriesForestRegressor(n_estimators=150)
+    >>> y_pred = regressor.predict(X_test) # doctest: +SKIP
     """
 
     _tags = {