Add regression, tests and refactor into base class

.github/workflows/ci.yml

@@ -8,18 +8,18 @@ jobs:
       - uses: actions/checkout@v2
       - uses: actions/setup-python@v2
         with:
-          python-version: 3.x
+          python-version: 3.9
       - run: python -m pip install --upgrade pip
-      - run: pip install mkdocs-material mkdocstrings docstring-parser
       - run: pip install -e .
+      - run: pip install mkdocs-material mkdocstrings docstring-parser
       - run: mkdocs gh-deploy --force
   lint:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v2
       - uses: actions/setup-python@v2
         with:
-          python-version: 3.x
+          python-version: 3.9
       - run: python -m pip install --upgrade pip
       - run: pip install black
       - run: black --check groot/
@@ -29,7 +29,7 @@ jobs:
       - uses: actions/checkout@v2
       - uses: actions/setup-python@v2
         with:
-          python-version: 3.x
+          python-version: 3.9
       - run: python -m pip install --upgrade pip
       - run: pip install -e .
       - run: pip install pytest

docs/reference/models/groot_forest.md

@@ -1,3 +1,17 @@
-The `GrootRandomForest` class uses bootstrap aggregation and partially random feature selection to train an ensemble of `GrootTree`s. On datasets with many features, a `GrootRandomForest` might perform better than a `GrootTree` as it is not limited in the number of features it can use by a maximum size.
+The `GrootRandomForestClassifier` class uses bootstrap aggregation and partially random feature selection to train an ensemble of `GrootTreeClassifier`s. On datasets with many features, a `GrootRandomForestClassifier` might perform better than a `GrootTreeClassifier` as it is not limited in the number of features it can use by a maximum size.
 
-::: groot.model:GrootRandomForest
+**Example:**
+```python
+from sklearn.datasets import make_moons
+X, y = make_moons(random_state=1)
+
+from groot.model import GrootRandomForestClassifier
+forest = GrootRandomForestClassifier(attack_model=[0.1, 0.1], random_state=1)
+forest.fit(X, y)
+print(forest.score(X, y))
+```
+```
+1.0
+```
+
+::: groot.model:GrootRandomForestClassifier

docs/reference/models/groot_tree.md

@@ -1,3 +1,17 @@
-The main class in the GROOT repository is the `GrootTree`, this class implements GROOT as a Scikit-learn compatible classifier. That means you initialize it with all important hyperparameters, then fit it using `.fit(X, y)` and predict with `.predict(X)` or `.predict_proba(X)`. The `GrootTree` is also used within the `GrootRandomForest`.
+The main class in the GROOT repository is the `GrootTreeClassifier`, this class implements GROOT as a Scikit-learn compatible classifier. That means you initialize it with all important hyperparameters, then fit it using `.fit(X, y)` and predict with `.predict(X)` or `.predict_proba(X)`. The `GrootTreeClassifier` is also used within the `GrootRandomForestClassifier`.
 
-::: groot.model:GrootTree
+**Example:**
+```python
+from sklearn.datasets import make_moons
+X, y = make_moons(random_state=1)
+
+from groot.model import GrootTreeClassifier
+tree = GrootTreeClassifier(max_depth=3, attack_model=[0.1, 0.1])
+tree.fit(X, y)
+print(tree.score(X, y))
+```
+```
+0.9
+```
+
+::: groot.model:GrootTreeClassifier