diff --git a/nbs/core/decision-tree.ipynb b/nbs/core/decision-tree.ipynb
index 16e7d9b..4b1e193 100644
--- a/nbs/core/decision-tree.ipynb
+++ b/nbs/core/decision-tree.ipynb
@@ -63,11 +63,11 @@
     "import seaborn as sns\n",
     "import sklearn.datasets as sk_datasets\n",
     "\n",
-    "from random_tree_models.decisiontree import (\n",
+    "from random_tree_models.models.decisiontree import (\n",
     "    DecisionTreeClassifier,\n",
     "    DecisionTreeRegressor,\n",
     ")\n",
-    "from random_tree_models.decisiontree.visualize import show_tree\n",
+    "from random_tree_models.models.decisiontree.visualize import show_tree\n",
     "from random_tree_models.scoring import MetricNames"
    ]
   },
diff --git a/nbs/core/extra-trees.ipynb b/nbs/core/extra-trees.ipynb
index ed44855..77e2a0b 100644
--- a/nbs/core/extra-trees.ipynb
+++ b/nbs/core/extra-trees.ipynb
@@ -41,10 +41,10 @@
     "import seaborn as sns\n",
     "import sklearn.datasets as sk_datasets\n",
     "\n",
-    "import random_tree_models.extratrees as et\n",
+    "import random_tree_models.models.extratrees as et\n",
     "from random_tree_models.scoring import MetricNames\n",
     "from random_tree_models.params import ThresholdSelectionMethod\n",
-    "from random_tree_models.decisiontree.visualize import show_tree"
+    "from random_tree_models.models.decisiontree.visualize import show_tree"
    ]
   },
   {
diff --git a/nbs/core/gradient-boosted-trees.ipynb b/nbs/core/gradient-boosted-trees.ipynb
index 81bd07e..dfd0384 100644
--- a/nbs/core/gradient-boosted-trees.ipynb
+++ b/nbs/core/gradient-boosted-trees.ipynb
@@ -71,8 +71,8 @@
     "import seaborn as sns\n",
     "import sklearn.datasets as sk_datasets\n",
     "\n",
-    "from random_tree_models.decisiontree.visualize import show_tree\n",
-    "import random_tree_models.gradientboostedtrees as gbtree\n",
+    "from random_tree_models.models.decisiontree.visualize import show_tree\n",
+    "import random_tree_models.models.gradientboostedtrees as gbtree\n",
     "from random_tree_models.scoring import MetricNames"
    ]
   },
diff --git a/nbs/core/isolation-forest.ipynb b/nbs/core/isolation-forest.ipynb
index 5ced2af..c19fd32 100644
--- a/nbs/core/isolation-forest.ipynb
+++ b/nbs/core/isolation-forest.ipynb
@@ -56,8 +56,8 @@
     "import seaborn as sns\n",
     "import sklearn.datasets as sk_datasets\n",
     "\n",
-    "from random_tree_models.decisiontree.visualize import show_tree\n",
-    "import random_tree_models.isolationforest as iforest\n",
+    "from random_tree_models.models.decisiontree.visualize import show_tree\n",
+    "import random_tree_models.models.isolationforest as iforest\n",
     "from random_tree_models.params import ColumnSelectionMethod, ThresholdSelectionMethod"
    ]
   },
diff --git a/nbs/core/random-forest.ipynb b/nbs/core/random-forest.ipynb
index 9f5448b..099183a 100644
--- a/nbs/core/random-forest.ipynb
+++ b/nbs/core/random-forest.ipynb
@@ -41,8 +41,8 @@
     "import seaborn as sns\n",
     "import sklearn.datasets as sk_datasets\n",
     "\n",
-    "from random_tree_models.decisiontree.visualize import show_tree\n",
-    "import random_tree_models.randomforest as rf\n",
+    "from random_tree_models.models.decisiontree.visualize import show_tree\n",
+    "import random_tree_models.models.randomforest as rf\n",
     "from random_tree_models.params import MetricNames"
    ]
   },
diff --git a/nbs/core/robust-random-cut-forest.ipynb b/nbs/core/robust-random-cut-forest.ipynb
index 034230f..16e6a79 100644
--- a/nbs/core/robust-random-cut-forest.ipynb
+++ b/nbs/core/robust-random-cut-forest.ipynb
@@ -55,8 +55,8 @@
     "import seaborn as sns\n",
     "import sklearn.datasets as sk_datasets\n",
     "\n",
-    "from random_tree_models.decisiontree.visualize import show_tree\n",
-    "import random_tree_models.isolationforest as iforest\n",
+    "from random_tree_models.models.decisiontree.visualize import show_tree\n",
+    "import random_tree_models.models.isolationforest as iforest\n",
     "from random_tree_models.params import ColumnSelectionMethod, ThresholdSelectionMethod"
    ]
   },
diff --git a/nbs/core/xgboost.ipynb b/nbs/core/xgboost.ipynb
index 4813e46..ec6b05a 100644
--- a/nbs/core/xgboost.ipynb
+++ b/nbs/core/xgboost.ipynb
@@ -284,8 +284,8 @@
     "import sklearn.datasets as sk_datasets\n",
     "from scipy import stats\n",
     "\n",
-    "from random_tree_models.decisiontree.visualize import show_tree\n",
-    "import random_tree_models.xgboost as xgboost\n",
+    "from random_tree_models.models.decisiontree.visualize import show_tree\n",
+    "import random_tree_models.models.xgboost as xgboost\n",
     "from random_tree_models.params import MetricNames"
    ]
   },
diff --git a/nbs/dev/xgboost-profiling-histogramming-yay-or-nay.ipynb b/nbs/dev/xgboost-profiling-histogramming-yay-or-nay.ipynb
index 2b3ddf7..cb0645e 100644
--- a/nbs/dev/xgboost-profiling-histogramming-yay-or-nay.ipynb
+++ b/nbs/dev/xgboost-profiling-histogramming-yay-or-nay.ipynb
@@ -49,7 +49,7 @@
     "import sklearn.datasets as sk_datasets\n",
     "from sklearn import metrics\n",
     "\n",
-    "import random_tree_models.xgboost as xgboost"
+    "import random_tree_models.models.xgboost as xgboost"
    ]
   },
   {
diff --git a/tests/decisiontree/__init__.py b/src/random_tree_models/models/__init__.py
similarity index 100%
rename from tests/decisiontree/__init__.py
rename to src/random_tree_models/models/__init__.py
diff --git a/src/random_tree_models/decisiontree/__init__.py b/src/random_tree_models/models/decisiontree/__init__.py
similarity index 100%
rename from src/random_tree_models/decisiontree/__init__.py
rename to src/random_tree_models/models/decisiontree/__init__.py
diff --git a/src/random_tree_models/decisiontree/estimators.py b/src/random_tree_models/models/decisiontree/estimators.py
similarity index 83%
rename from src/random_tree_models/decisiontree/estimators.py
rename to src/random_tree_models/models/decisiontree/estimators.py
index df267d2..cd5b37f 100644
--- a/src/random_tree_models/decisiontree/estimators.py
+++ b/src/random_tree_models/models/decisiontree/estimators.py
@@ -2,12 +2,17 @@
 
 import numpy as np
 import sklearn.base as base
+from sklearn.utils import ClassifierTags  # type: ignore
 from sklearn.utils.multiclass import check_classification_targets, type_of_target
 from sklearn.utils.validation import check_is_fitted, validate_data  # type: ignore
 
-from random_tree_models.decisiontree.node import Node
-from random_tree_models.decisiontree.predict import predict_with_tree
-from random_tree_models.decisiontree.train import grow_tree
+from random_tree_models.models.decisiontree.node import Node
+from random_tree_models.models.decisiontree.predict import predict_with_tree
+from random_tree_models.models.decisiontree.random import (
+    get_random_feature_ids,
+    get_random_sample_ids,
+)
+from random_tree_models.models.decisiontree.train import grow_tree
 from random_tree_models.params import (
     ColumnSelectionMethod,
     ColumnSelectionParameters,
@@ -55,6 +60,7 @@ def __init__(
         random_state: int = 42,
         ensure_all_finite: bool = True,
     ) -> None:
+        # scikit-learn requires we store parameters like this below, instead of directly assigning TreeGrowthParameters
         self.max_depth = max_depth
         self.measure_name = measure_name
         self.min_improvement = min_improvement
@@ -70,49 +76,43 @@ def __init__(
         self.ensure_all_finite = ensure_all_finite
 
     def _organize_growth_parameters(self):
+        lam = -abs(self.lam)  # doing this for probably a good reason
+
+        threshold_params = ThresholdSelectionParameters(
+            method=self.threshold_method,
+            quantile=self.threshold_quantile,
+            n_thresholds=self.n_thresholds,
+            random_state=self.random_state,
+        )
+
+        column_params = ColumnSelectionParameters(
+            method=self.column_method,
+            n_trials=self.n_columns_to_try,
+        )
+
         self.growth_params_ = TreeGrowthParameters(
             max_depth=self.max_depth,
             min_improvement=self.min_improvement,
-            lam=-abs(self.lam),
-            frac_subsamples=float(self.frac_subsamples),
-            frac_features=float(self.frac_features),
-            random_state=int(self.random_state),
-            threshold_params=ThresholdSelectionParameters(
-                method=self.threshold_method,
-                quantile=self.threshold_quantile,
-                n_thresholds=self.n_thresholds,
-                random_state=int(self.random_state),
-            ),
-            column_params=ColumnSelectionParameters(
-                method=self.column_method,
-                n_trials=self.n_columns_to_try,
-            ),
+            lam=lam,
+            frac_subsamples=self.frac_subsamples,
+            frac_features=self.frac_features,
+            random_state=self.random_state,
+            threshold_params=threshold_params,
+            column_params=column_params,
         )
 
     def _select_samples_and_features(
         self, X: np.ndarray, y: np.ndarray
     ) -> T.Tuple[np.ndarray, np.ndarray, np.ndarray]:
         "Sub-samples rows and columns from X and y"
+
         if not hasattr(self, "growth_params_"):
             raise ValueError(f"Try calling `fit` first.")
 
-        ix = np.arange(len(X))
         rng = np.random.RandomState(self.growth_params_.random_state)
-        if self.growth_params_.frac_subsamples < 1.0:
-            n_samples = int(self.growth_params_.frac_subsamples * len(X))
-            ix_samples = rng.choice(ix, size=n_samples, replace=False)
-        else:
-            ix_samples = ix
-
-        if self.frac_features < 1.0:
-            n_columns = int(X.shape[1] * self.frac_features)
-            ix_features = rng.choice(
-                np.arange(X.shape[1]),
-                size=n_columns,
-                replace=False,
-            )
-        else:
-            ix_features = np.arange(X.shape[1])
+
+        ix_samples = get_random_sample_ids(X, rng, self.growth_params_.frac_subsamples)
+        ix_features = get_random_feature_ids(X, rng, self.growth_params_.frac_features)
 
         _X = X[ix_samples, :]
         _X = _X[:, ix_features]
@@ -245,16 +245,9 @@ def __init__(
         )
         self.ensure_all_finite = ensure_all_finite
 
-    def _more_tags(self) -> T.Dict[str, bool]:
-        """Describes to scikit-learn parametrize_with_checks the scope of this class
-
-        Reference: https://scikit-learn.org/stable/developers/develop.html#estimator-tags
-        """
-        return {"binary_only": True}
-
     def __sklearn_tags__(self):
-        # https://scikit-learn.org/stable/developers/develop.html
-        tags = super().__sklearn_tags__()  # type: ignore
+        # https://scikit-learn.org/stable/developers/develop.html#estimator-tags
+        tags: ClassifierTags = super().__sklearn_tags__()  # type: ignore
         tags.classifier_tags.multi_class = False
         return tags
 
diff --git a/src/random_tree_models/decisiontree/node.py b/src/random_tree_models/models/decisiontree/node.py
similarity index 93%
rename from src/random_tree_models/decisiontree/node.py
rename to src/random_tree_models/models/decisiontree/node.py
index 9489d14..5068acd 100644
--- a/src/random_tree_models/decisiontree/node.py
+++ b/src/random_tree_models/models/decisiontree/node.py
@@ -3,7 +3,7 @@
 from pydantic import StrictInt, StrictStr
 from pydantic.dataclasses import dataclass
 
-from random_tree_models.decisiontree.split_objects import SplitScore
+from random_tree_models.models.decisiontree.split_objects import SplitScore
 
 
 @dataclass
diff --git a/src/random_tree_models/decisiontree/predict.py b/src/random_tree_models/models/decisiontree/predict.py
similarity index 96%
rename from src/random_tree_models/decisiontree/predict.py
rename to src/random_tree_models/models/decisiontree/predict.py
index 59fac01..06a7fac 100644
--- a/src/random_tree_models/decisiontree/predict.py
+++ b/src/random_tree_models/models/decisiontree/predict.py
@@ -1,6 +1,6 @@
 import numpy as np
 
-from random_tree_models.decisiontree.node import Node
+from random_tree_models.models.decisiontree.node import Node
 
 
 def find_leaf_node(node: Node, x: np.ndarray) -> Node:
diff --git a/src/random_tree_models/models/decisiontree/random.py b/src/random_tree_models/models/decisiontree/random.py
new file mode 100644
index 0000000..fa9fa55
--- /dev/null
+++ b/src/random_tree_models/models/decisiontree/random.py
@@ -0,0 +1,28 @@
+import numpy as np
+
+
+def get_random_sample_ids(
+    X: np.ndarray, rng: np.random.RandomState, frac_subsamples: float
+) -> np.ndarray:
+    ix = np.arange(len(X))
+    if frac_subsamples < 1.0:
+        n_samples = int(frac_subsamples * len(X))
+        ix_samples = rng.choice(ix, size=n_samples, replace=False)
+    else:
+        ix_samples = ix
+    return ix_samples
+
+
+def get_random_feature_ids(
+    X: np.ndarray, rng: np.random.RandomState, frac_features: float
+) -> np.ndarray:
+    if frac_features < 1.0:
+        n_columns = int(X.shape[1] * frac_features)
+        ix_features = rng.choice(
+            np.arange(X.shape[1]),
+            size=n_columns,
+            replace=False,
+        )
+    else:
+        ix_features = np.arange(X.shape[1])
+    return ix_features
diff --git a/src/random_tree_models/decisiontree/split.py b/src/random_tree_models/models/decisiontree/split.py
similarity index 98%
rename from src/random_tree_models/decisiontree/split.py
rename to src/random_tree_models/models/decisiontree/split.py
index 25716a9..a01bf14 100644
--- a/src/random_tree_models/decisiontree/split.py
+++ b/src/random_tree_models/models/decisiontree/split.py
@@ -3,8 +3,8 @@
 import numpy as np
 
 import random_tree_models.scoring as scoring
-from random_tree_models.decisiontree.node import Node
-from random_tree_models.decisiontree.split_objects import BestSplit
+from random_tree_models.models.decisiontree.node import Node
+from random_tree_models.models.decisiontree.split_objects import BestSplit
 from random_tree_models.params import (
     ColumnSelectionMethod,
     ColumnSelectionParameters,
diff --git a/src/random_tree_models/decisiontree/split_objects.py b/src/random_tree_models/models/decisiontree/split_objects.py
similarity index 100%
rename from src/random_tree_models/decisiontree/split_objects.py
rename to src/random_tree_models/models/decisiontree/split_objects.py
diff --git a/src/random_tree_models/decisiontree/train.py b/src/random_tree_models/models/decisiontree/train.py
similarity index 96%
rename from src/random_tree_models/decisiontree/train.py
rename to src/random_tree_models/models/decisiontree/train.py
index 1f33cc1..15f982a 100644
--- a/src/random_tree_models/decisiontree/train.py
+++ b/src/random_tree_models/models/decisiontree/train.py
@@ -5,13 +5,13 @@
 import random_tree_models.leafweights as leafweights
 import random_tree_models.params
 import random_tree_models.scoring as scoring
-from random_tree_models.decisiontree.node import Node
-from random_tree_models.decisiontree.split import (
+from random_tree_models.models.decisiontree.node import Node
+from random_tree_models.models.decisiontree.split import (
     check_if_split_sensible,
     find_best_split,
     select_arrays_for_child_node,
 )
-from random_tree_models.decisiontree.split_objects import SplitScore
+from random_tree_models.models.decisiontree.split_objects import SplitScore
 from random_tree_models.params import MetricNames
 
 
diff --git a/src/random_tree_models/decisiontree/visualize.py b/src/random_tree_models/models/decisiontree/visualize.py
similarity index 89%
rename from src/random_tree_models/decisiontree/visualize.py
rename to src/random_tree_models/models/decisiontree/visualize.py
index 8d4aa60..19717f2 100644
--- a/src/random_tree_models/decisiontree/visualize.py
+++ b/src/random_tree_models/models/decisiontree/visualize.py
@@ -1,8 +1,8 @@
 from rich import print as rprint
 from rich.tree import Tree
 
-from random_tree_models.decisiontree.estimators import DecisionTreeTemplate
-from random_tree_models.decisiontree.node import Node
+from random_tree_models.models.decisiontree.estimators import DecisionTreeTemplate
+from random_tree_models.models.decisiontree.node import Node
 
 
 def walk_tree(
diff --git a/src/random_tree_models/extratrees.py b/src/random_tree_models/models/extratrees.py
similarity index 96%
rename from src/random_tree_models/extratrees.py
rename to src/random_tree_models/models/extratrees.py
index 1a67dba..011abad 100644
--- a/src/random_tree_models/extratrees.py
+++ b/src/random_tree_models/models/extratrees.py
@@ -9,8 +9,11 @@
     validate_data,  # type: ignore
 )
 
-import random_tree_models.decisiontree as dtree
 import random_tree_models.params as utils
+from random_tree_models.models.decisiontree import (
+    DecisionTreeClassifier,
+    DecisionTreeRegressor,
+)
 from random_tree_models.params import MetricNames
 
 
@@ -103,11 +106,11 @@ def __init__(
     def fit(self, X: np.ndarray, y: np.ndarray) -> "ExtraTreesRegressor":
         X, y = validate_data(self, X, y, ensure_all_finite=False)
 
-        self.trees_: T.List[dtree.DecisionTreeRegressor] = []
+        self.trees_: list[DecisionTreeRegressor] = []
         rng = np.random.RandomState(self.random_state)
         for _ in track(range(self.n_trees), total=self.n_trees, description="tree"):
             # train decision tree to predict differences
-            new_tree = dtree.DecisionTreeRegressor(
+            new_tree = DecisionTreeRegressor(
                 measure_name=self.measure_name,
                 max_depth=self.max_depth,
                 min_improvement=self.min_improvement,
@@ -199,11 +202,11 @@ def fit(self, X: np.ndarray, y: np.ndarray) -> "ExtraTreesClassifier":
             raise ValueError("Cannot train with only one class present")
 
         self.classes_, y = np.unique(y, return_inverse=True)
-        self.trees_: T.List[dtree.DecisionTreeClassifier] = []
+        self.trees_: list[DecisionTreeClassifier] = []
 
         rng = np.random.RandomState(self.random_state)
         for _ in track(range(self.n_trees), description="tree", total=self.n_trees):
-            new_tree = dtree.DecisionTreeClassifier(
+            new_tree = DecisionTreeClassifier(
                 measure_name=self.measure_name,
                 max_depth=self.max_depth,
                 min_improvement=self.min_improvement,
diff --git a/src/random_tree_models/gradientboostedtrees.py b/src/random_tree_models/models/gradientboostedtrees.py
similarity index 95%
rename from src/random_tree_models/gradientboostedtrees.py
rename to src/random_tree_models/models/gradientboostedtrees.py
index 9857378..46d7d40 100644
--- a/src/random_tree_models/gradientboostedtrees.py
+++ b/src/random_tree_models/models/gradientboostedtrees.py
@@ -13,8 +13,10 @@
     validate_data,  # type: ignore
 )
 
-import random_tree_models.decisiontree as dtree
-from random_tree_models.params import MetricNames
+from random_tree_models.models.decisiontree import (
+    DecisionTreeRegressor,
+)
+from random_tree_models.params import MetricNames, is_greater_zero
 from random_tree_models.utils import bool_to_float
 
 
@@ -33,7 +35,7 @@ def __init__(
         min_improvement: float = 0.0,
         ensure_all_finite: bool = True,
     ) -> None:
-        self.n_trees = n_trees
+        self.n_trees = is_greater_zero(n_trees)
         self.measure_name = measure_name
         self.max_depth = max_depth
         self.min_improvement = min_improvement
@@ -94,7 +96,7 @@ def __init__(
     def fit(self, X: np.ndarray, y: np.ndarray) -> "GradientBoostedTreesRegressor":
         X, y = validate_data(self, X, y, ensure_all_finite=False)
 
-        self.trees_: T.List[dtree.DecisionTreeRegressor] = []
+        self.trees_: list[DecisionTreeRegressor] = []
 
         self.start_estimate_ = np.mean(y)
 
@@ -103,7 +105,7 @@ def fit(self, X: np.ndarray, y: np.ndarray) -> "GradientBoostedTreesRegressor":
 
         for _ in track(range(self.n_trees), total=self.n_trees, description="tree"):
             # train decision tree to predict differences
-            new_tree = dtree.DecisionTreeRegressor(
+            new_tree = DecisionTreeRegressor(
                 measure_name=self.measure_name,
                 max_depth=self.max_depth,
                 min_improvement=self.min_improvement,
@@ -239,9 +241,8 @@ def fit(self, X: np.ndarray, y: np.ndarray) -> "GradientBoostedTreesClassifier":
         if len(np.unique(y)) == 1:
             raise ValueError("Cannot train with only one class present")
 
-        # self.n_features_in_ = X.shape[1]
         self.classes_, y = np.unique(y, return_inverse=True)
-        self.trees_: T.List[dtree.DecisionTreeRegressor] = []
+        self.trees_: list[DecisionTreeRegressor] = []
         self.gammas_ = []
 
         y = self._bool_to_float(y)
@@ -255,7 +256,7 @@ def fit(self, X: np.ndarray, y: np.ndarray) -> "GradientBoostedTreesClassifier":
                 2 * y / (1 + np.exp(2 * y * yhat))
             )  # dloss/dyhat, g in the xgboost paper
 
-            new_tree = dtree.DecisionTreeRegressor(
+            new_tree = DecisionTreeRegressor(
                 measure_name=self.measure_name,
                 max_depth=self.max_depth,
                 min_improvement=self.min_improvement,
diff --git a/src/random_tree_models/isolationforest.py b/src/random_tree_models/models/isolationforest.py
similarity index 98%
rename from src/random_tree_models/isolationforest.py
rename to src/random_tree_models/models/isolationforest.py
index 4be543d..6ee842c 100644
--- a/src/random_tree_models/isolationforest.py
+++ b/src/random_tree_models/models/isolationforest.py
@@ -5,12 +5,12 @@
 from sklearn import base
 from sklearn.utils.validation import check_is_fitted, validate_data  # type: ignore
 
-from random_tree_models.decisiontree import (
+from random_tree_models.models.decisiontree import (
     DecisionTreeTemplate,
     find_leaf_node,
     grow_tree,
 )
-from random_tree_models.decisiontree.node import Node
+from random_tree_models.models.decisiontree.node import Node
 from random_tree_models.params import (
     ColumnSelectionMethod,
     MetricNames,
diff --git a/src/random_tree_models/randomforest.py b/src/random_tree_models/models/randomforest.py
similarity index 86%
rename from src/random_tree_models/randomforest.py
rename to src/random_tree_models/models/randomforest.py
index dbe0164..f7015b3 100644
--- a/src/random_tree_models/randomforest.py
+++ b/src/random_tree_models/models/randomforest.py
@@ -9,8 +9,11 @@
     validate_data,  # type: ignore
 )
 
-import random_tree_models.decisiontree as dtree
-from random_tree_models.params import MetricNames
+from random_tree_models.models.decisiontree import (
+    DecisionTreeClassifier,
+    DecisionTreeRegressor,
+)
+from random_tree_models.params import MetricNames, is_greater_zero
 
 
 class RandomForestTemplate(base.BaseEstimator):
@@ -34,7 +37,7 @@ def __init__(
         frac_features: float = 1.0,
         random_state: int = 42,
     ) -> None:
-        self.n_trees = n_trees
+        self.n_trees = is_greater_zero(n_trees)
         self.measure_name = measure_name
         self.max_depth = max_depth
         self.min_improvement = min_improvement
@@ -87,10 +90,10 @@ def __init__(
     def fit(self, X: np.ndarray, y: np.ndarray) -> "RandomForestRegressor":
         X, y = validate_data(self, X, y, ensure_all_finite=False)
 
-        self.trees_: T.List[dtree.DecisionTreeRegressor] = []
+        self.trees_: list[DecisionTreeRegressor] = []
         rng = np.random.RandomState(self.random_state)
         for _ in track(range(self.n_trees), total=self.n_trees, description="tree"):
-            new_tree = dtree.DecisionTreeRegressor(
+            new_tree = DecisionTreeRegressor(
                 measure_name=self.measure_name,
                 max_depth=self.max_depth,
                 min_improvement=self.min_improvement,
@@ -116,10 +119,13 @@ def predict(self, X: np.ndarray, aggregation: str = "mean") -> np.ndarray:
         ):
             y[:, i] = tree.predict(X)
 
-        if aggregation == "mean":
-            y = np.mean(y, axis=1)
-        elif aggregation == "median":
-            y = np.median(y, axis=1)
+        match aggregation:
+            case "mean":
+                y = np.mean(y, axis=1)
+            case "median":
+                y = np.median(y, axis=1)
+            case _:
+                raise ValueError(f"{aggregation=} expected to be 'mean' or 'median'")
 
         return y
 
@@ -181,11 +187,11 @@ def fit(self, X: np.ndarray, y: np.ndarray) -> "RandomForestClassifier":
             raise ValueError("Cannot train with only one class present")
 
         self.classes_, y = np.unique(y, return_inverse=True)
-        self.trees_: T.List[dtree.DecisionTreeClassifier] = []
+        self.trees_: list[DecisionTreeClassifier] = []
 
         rng = np.random.RandomState(self.random_state)
         for _ in track(range(self.n_trees), description="tree", total=self.n_trees):
-            new_tree = dtree.DecisionTreeClassifier(
+            new_tree = DecisionTreeClassifier(
                 measure_name=self.measure_name,
                 max_depth=self.max_depth,
                 min_improvement=self.min_improvement,
@@ -211,12 +217,15 @@ def predict_proba(self, X: np.ndarray, aggregation: str = "mean") -> np.ndarray:
         ):
             proba[:, i, :] = tree.predict_proba(X)
 
-        if aggregation == "mean":
-            proba = np.mean(proba, axis=1)
-            proba = proba / np.sum(proba, axis=1)[:, np.newaxis]
-        elif aggregation == "median":
-            proba = np.median(proba, axis=1)
-            proba = proba / np.sum(proba, axis=1)[:, np.newaxis]
+        match aggregation:
+            case "mean":
+                proba = np.mean(proba, axis=1)
+                proba = proba / np.sum(proba, axis=1)[:, np.newaxis]
+            case "median":
+                proba = np.median(proba, axis=1)
+                proba = proba / np.sum(proba, axis=1)[:, np.newaxis]
+            case _:
+                raise ValueError(f"{aggregation=} expected to be 'mean' or 'median'")
 
         return proba
 
diff --git a/src/random_tree_models/xgboost.py b/src/random_tree_models/models/xgboost.py
similarity index 88%
rename from src/random_tree_models/xgboost.py
rename to src/random_tree_models/models/xgboost.py
index a227f08..2d42d32 100644
--- a/src/random_tree_models/xgboost.py
+++ b/src/random_tree_models/models/xgboost.py
@@ -26,9 +26,9 @@
     validate_data,  # type: ignore
 )
 
-import random_tree_models.decisiontree as dtree
-import random_tree_models.utils as gbt
-from random_tree_models.params import MetricNames
+from random_tree_models.models.decisiontree import DecisionTreeRegressor
+from random_tree_models.params import MetricNames, is_greater_zero
+from random_tree_models.utils import vectorize_bool_to_float
 
 
 class XGBoostTemplate(base.BaseEstimator):
@@ -52,7 +52,7 @@ def __init__(
         use_hist: bool = False,
         n_bins: int = 256,
     ) -> None:
-        self.n_trees = n_trees
+        self.n_trees = is_greater_zero(n_trees)
         self.measure_name = measure_name
         self.max_depth = max_depth
         self.min_improvement = min_improvement
@@ -92,8 +92,10 @@ def xgboost_histogrammify_with_h(
     X: np.ndarray, h: np.ndarray, n_bins: int
 ) -> T.Tuple[np.ndarray, T.List[np.ndarray]]:
     """Converts X into a histogram representation using XGBoost paper eq 8 and 9 using 2nd order gradient statistics as weights"""
+
     X_hist = np.zeros_like(X, dtype=int)
     all_x_bin_edges = []
+
     for i in range(X.shape[1]):
         order = np.argsort(X[:, i])
         h_ordered = h[order]
@@ -122,12 +124,12 @@ def xgboost_histogrammify_with_x_bin_edges(
     X: np.ndarray, all_x_bin_edges: T.List[np.ndarray]
 ) -> np.ndarray:
     """Converts X into a histogram representation using XGBoost paper eq 8 and 9 using 2nd order gradient statistics as weights"""
+
     X_hist = np.zeros_like(X, dtype=int)
 
     for i in range(X.shape[1]):
-        bin_assignments = pd.cut(
-            X[:, i], bins=all_x_bin_edges[i].tolist(), labels=False, include_lowest=True
-        )
+        bins = all_x_bin_edges[i].tolist()
+        bin_assignments = pd.cut(X[:, i], bins=bins, labels=False, include_lowest=True)
 
         X_hist[:, i] = bin_assignments
 
@@ -143,10 +145,8 @@ class XGBoostRegressor(base.RegressorMixin, XGBoostTemplate):
 
     def fit(self, X: np.ndarray, y: np.ndarray) -> "XGBoostRegressor":
         X, y = validate_data(self, X, y, ensure_all_finite=False)
-        # X, y = check_X_y(X, y, ensure_all_finite=self.ensure_all_finite)
-        # self.n_features_in_ = X.shape[1]
 
-        self.trees_: T.List[dtree.DecisionTreeRegressor] = []
+        self.trees_: list[DecisionTreeRegressor] = []
 
         self.start_estimate_: float = float(np.mean(y))
 
@@ -161,7 +161,7 @@ def fit(self, X: np.ndarray, y: np.ndarray) -> "XGBoostRegressor":
 
         for _ in track(range(self.n_trees), total=self.n_trees, description="tree"):
             # train decision tree to predict differences
-            new_tree = dtree.DecisionTreeRegressor(
+            new_tree = DecisionTreeRegressor(
                 measure_name=self.measure_name,
                 max_depth=self.max_depth,
                 min_improvement=self.min_improvement,
@@ -211,6 +211,7 @@ def compute_start_estimate_binomial_loglikelihood(y_float: np.ndarray) -> float:
 
     ym = np.mean(y_float)
     start_estimate = 0.5 * math.log((1 + ym) / (1 - ym))
+
     return start_estimate
 
 
@@ -218,13 +219,16 @@ def compute_derivatives_binomial_loglikelihood(
     y_float: np.ndarray, yhat: np.ndarray
 ) -> T.Tuple[np.ndarray, np.ndarray]:
     "loss = - sum log(1+exp(2*y*yhat))"
+
     check_y_float(y_float)
+
     # differences to predict using binomial log-likelihood (yes, the negative of the negative :P)
     exp_y_yhat = np.exp(2 * y_float * yhat)
     g = 2 * y_float / (1 + exp_y_yhat)  # dloss/dyhat, g in the xgboost paper
-    h = -(
-        4 * y_float**2 * exp_y_yhat / (1 + exp_y_yhat) ** 2
-    )  # d^2loss/dyhat^2, h in the xgboost paper
+
+    # d^2loss/dyhat^2, h in the xgboost paper
+    h = -(4 * y_float**2 * exp_y_yhat / (1 + exp_y_yhat) ** 2)
+
     return g, h
 
 
@@ -235,17 +239,6 @@ class XGBoostClassifier(base.ClassifierMixin, XGBoostTemplate):
     https://dl.acm.org/doi/10.1145/2939672.2939785
     """
 
-    def _bool_to_float(self, y: np.ndarray) -> np.ndarray:
-        f = np.vectorize(gbt.bool_to_float)
-        return f(y)
-
-    def _more_tags(self) -> T.Dict[str, bool]:
-        """Describes to scikit-learn parametrize_with_checks the scope of this class
-
-        Reference: https://scikit-learn.org/stable/developers/develop.html#estimator-tags
-        """
-        return {"binary_only": True}
-
     def __sklearn_tags__(self):
         # https://scikit-learn.org/stable/developers/develop.html
         tags = super().__sklearn_tags__()  # type: ignore
@@ -267,14 +260,13 @@ def fit(self, X: np.ndarray, y: np.ndarray) -> "XGBoostClassifier":
         if len(np.unique(y)) == 1:
             raise ValueError("Cannot train with only one class present")
 
-        # self.n_features_in_ = X.shape[1]
         self.classes_, y = np.unique(y, return_inverse=True)
-        self.trees_: T.List[dtree.DecisionTreeRegressor] = []
+        self.trees_: list[DecisionTreeRegressor] = []
         self.gammas_ = []
         self.all_x_bin_edges_ = []
 
         # convert y from True/False to 1/-1 for binomial log-likelihood
-        y = self._bool_to_float(y)
+        y = vectorize_bool_to_float(y)
 
         # initial estimate
         self.start_estimate_ = compute_start_estimate_binomial_loglikelihood(y)
@@ -291,7 +283,7 @@ def fit(self, X: np.ndarray, y: np.ndarray) -> "XGBoostClassifier":
             else:
                 _X = X
 
-            new_tree = dtree.DecisionTreeRegressor(
+            new_tree = DecisionTreeRegressor(
                 measure_name=self.measure_name,
                 max_depth=self.max_depth,
                 min_improvement=self.min_improvement,
diff --git a/src/random_tree_models/params.py b/src/random_tree_models/params.py
index b261bb8..5b44704 100644
--- a/src/random_tree_models/params.py
+++ b/src/random_tree_models/params.py
@@ -1,7 +1,7 @@
 from enum import StrEnum, auto
-from typing import Any
+from typing import Annotated, Any
 
-from pydantic import BaseModel, StrictFloat, StrictInt
+from pydantic import AfterValidator, BaseModel, StrictFloat, StrictInt
 
 
 class ColumnSelectionMethod(StrEnum):
@@ -17,40 +17,54 @@ class ThresholdSelectionMethod(StrEnum):
     uniform = "uniform"
 
 
+def is_quantile(quantile: float) -> float:
+    is_okay = 0.0 < quantile < 1.0
+    if not is_okay:
+        raise ValueError(f"{quantile=} not in (0, 1)")
+
+    is_okay = 1 / quantile % 1 == 0
+    if not is_okay:
+        raise ValueError(f"{quantile=} not a valid quantile")
+
+    return quantile
+
+
+def is_fraction(fraction: float) -> float:
+    is_okay = 0.0 < fraction <= 1.0
+    if not is_okay:
+        raise ValueError(f"{fraction=} not in (0, 1]")
+
+    return fraction
+
+
+def is_greater_zero(value: int) -> int:
+    if value <= 0:
+        raise ValueError(f"{value=} not > 0")
+    return value
+
+
+def is_greater_equal_zero(value: int | float) -> int | float:
+    if value < 0:
+        raise ValueError(f"{value=} not >= 0")
+    return value
+
+
+QuantileValidator = AfterValidator(is_quantile)
+GreaterEqualZeroValidator = AfterValidator(is_greater_equal_zero)
+GreaterZeroValidator = AfterValidator(is_greater_zero)
+
+
 class ThresholdSelectionParameters(BaseModel):
     method: ThresholdSelectionMethod
-    quantile: StrictFloat = 0.1
-    random_state: StrictInt = 0
-    n_thresholds: StrictInt = 100
+
+    quantile: Annotated[StrictFloat, QuantileValidator] = 0.1
+
+    random_state: Annotated[StrictInt, GreaterEqualZeroValidator] = 0
+
+    n_thresholds: Annotated[StrictInt, GreaterZeroValidator] = 100
     num_quantile_steps: StrictInt = -1
 
     def model_post_init(self, context: Any):
-        # verify method
-        # expected = ThresholdSelectionMethod.__members__.keys()
-        # is_okay = self.method in expected
-        # if not is_okay:
-        #     raise ValueError(
-        #         f"passed value for method ('{self.method}') not one of {expected}"
-        #     )
-
-        # verify quantile
-        is_okay = 0.0 < self.quantile < 1.0
-        if not is_okay:
-            raise ValueError(f"{self.quantile=} not in (0, 1)")
-        is_okay = 1 / self.quantile % 1 == 0
-        if not is_okay:
-            raise ValueError(f"{self.quantile=} not a valid quantile")
-
-        # verify random_state
-        is_okay = self.random_state >= 0
-        if not is_okay:
-            raise ValueError(f"{self.random_state=} not in [0, inf)")
-
-        # verify n_thresholds valid int
-        is_okay = self.n_thresholds > 0
-        if not is_okay:
-            raise ValueError(f"{self.n_thresholds=} not > 0")
-
         # set dq
         self.num_quantile_steps = int(1 / self.quantile) + 1
 
@@ -60,15 +74,19 @@ class ColumnSelectionParameters(BaseModel):
     n_trials: StrictInt | None = None
 
 
+FractionValidator = AfterValidator(is_fraction)
+
+
 class TreeGrowthParameters(BaseModel):
-    max_depth: StrictInt
-    min_improvement: StrictFloat = 0.0
+    max_depth: Annotated[StrictInt, GreaterZeroValidator]
+    min_improvement: Annotated[StrictFloat, GreaterEqualZeroValidator] = 0.0
     # xgboost lambda - multiplied with sum of squares of leaf weights
     # see Chen et al. 2016 equation 2
     lam: StrictFloat = 0.0
-    frac_subsamples: StrictFloat = 1.0
-    frac_features: StrictFloat = 1.0
-    random_state: StrictInt = 0
+
+    frac_subsamples: Annotated[StrictFloat, FractionValidator] = 1.0
+    frac_features: Annotated[StrictFloat, FractionValidator] = 1.0
+    random_state: Annotated[StrictInt, GreaterEqualZeroValidator] = 0
     threshold_params: ThresholdSelectionParameters = ThresholdSelectionParameters(
         method=ThresholdSelectionMethod.bruteforce,
         quantile=0.1,
@@ -79,17 +97,6 @@ class TreeGrowthParameters(BaseModel):
         method=ColumnSelectionMethod.ascending, n_trials=None
     )
 
-    def model_post_init(self, context: Any):
-        # verify frac_subsamples
-        is_okay = 0.0 < self.frac_subsamples <= 1.0
-        if not is_okay:
-            raise ValueError(f"{self.frac_subsamples=} not in (0, 1]")
-
-        # verify frac_features
-        is_okay = 0.0 < self.frac_features <= 1.0
-        if not is_okay:
-            raise ValueError(f"{self.frac_features=} not in (0, 1]")
-
 
 class MetricNames(StrEnum):
     variance = auto()
diff --git a/src/random_tree_models/utils.py b/src/random_tree_models/utils.py
index 58bc1d3..390eec5 100644
--- a/src/random_tree_models/utils.py
+++ b/src/random_tree_models/utils.py
@@ -1,5 +1,6 @@
 import logging
 
+import numpy as np
 from rich.logging import RichHandler
 
 
@@ -28,3 +29,8 @@ def bool_to_float(x: bool) -> float:
         return -1.0
     else:
         raise ValueError(f"{x=}, expected bool")
+
+
+def vectorize_bool_to_float(y: np.ndarray) -> np.ndarray:
+    f = np.vectorize(bool_to_float)
+    return f(y)
diff --git a/tests/models/__init__.py b/tests/models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/models/decisiontree/__init__.py b/tests/models/decisiontree/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/decisiontree/conftest.py b/tests/models/decisiontree/conftest.py
similarity index 92%
rename from tests/decisiontree/conftest.py
rename to tests/models/decisiontree/conftest.py
index a86f141..b7edf9f 100644
--- a/tests/decisiontree/conftest.py
+++ b/tests/models/decisiontree/conftest.py
@@ -1,4 +1,4 @@
-from random_tree_models.decisiontree.node import Node
+from random_tree_models.models.decisiontree.node import Node
 
 # first value in each tuple is the value to test and the second is the flag indicating if this should work
 BOOL_OPTIONS_NONE_OKAY = [(False, True), (True, True), ("blub", False)]
diff --git a/tests/decisiontree/test_estimators.py b/tests/models/decisiontree/test_estimators.py
similarity index 96%
rename from tests/decisiontree/test_estimators.py
rename to tests/models/decisiontree/test_estimators.py
index 9f6c185..f725c7d 100644
--- a/tests/decisiontree/test_estimators.py
+++ b/tests/models/decisiontree/test_estimators.py
@@ -3,12 +3,12 @@
 from sklearn.utils.estimator_checks import parametrize_with_checks
 
 import random_tree_models.params
-from random_tree_models.decisiontree import (
+from random_tree_models.models.decisiontree import (
     DecisionTreeClassifier,
     DecisionTreeRegressor,
 )
-from random_tree_models.decisiontree.estimators import DecisionTreeTemplate
-from random_tree_models.decisiontree.node import Node
+from random_tree_models.models.decisiontree.estimators import DecisionTreeTemplate
+from random_tree_models.models.decisiontree.node import Node
 from random_tree_models.params import MetricNames
 from tests.conftest import expected_failed_checks
 
diff --git a/tests/decisiontree/test_node.py b/tests/models/decisiontree/test_node.py
similarity index 94%
rename from tests/decisiontree/test_node.py
rename to tests/models/decisiontree/test_node.py
index 6d92468..085e819 100644
--- a/tests/decisiontree/test_node.py
+++ b/tests/models/decisiontree/test_node.py
@@ -1,8 +1,8 @@
 import pytest
 from pydantic import ValidationError
 
-from random_tree_models.decisiontree.node import Node
-from random_tree_models.decisiontree.split_objects import SplitScore
+from random_tree_models.models.decisiontree.node import Node
+from random_tree_models.models.decisiontree.split_objects import SplitScore
 
 from .conftest import (
     BOOL_OPTIONS_NONE_OKAY,
diff --git a/tests/decisiontree/test_predict.py b/tests/models/decisiontree/test_predict.py
similarity index 89%
rename from tests/decisiontree/test_predict.py
rename to tests/models/decisiontree/test_predict.py
index f0806d7..d7dfbe7 100644
--- a/tests/decisiontree/test_predict.py
+++ b/tests/models/decisiontree/test_predict.py
@@ -1,8 +1,11 @@
 import numpy as np
 import pytest
 
-from random_tree_models.decisiontree.node import Node
-from random_tree_models.decisiontree.predict import find_leaf_node, predict_with_tree
+from random_tree_models.models.decisiontree.node import Node
+from random_tree_models.models.decisiontree.predict import (
+    find_leaf_node,
+    predict_with_tree,
+)
 
 
 @pytest.mark.parametrize(
diff --git a/tests/models/decisiontree/test_random.py b/tests/models/decisiontree/test_random.py
new file mode 100644
index 0000000..e50603b
--- /dev/null
+++ b/tests/models/decisiontree/test_random.py
@@ -0,0 +1,38 @@
+import numpy as np
+
+from random_tree_models.models.decisiontree.random import (
+    get_random_feature_ids,
+    get_random_sample_ids,
+)
+
+
+def test_get_random_sample_ids():
+    X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])
+    rng = np.random.RandomState(42)
+
+    # Test with frac_subsamples = 1.0
+    ix_samples_full = get_random_sample_ids(X, rng, 1.0)
+    assert np.array_equal(ix_samples_full, np.array([0, 1, 2, 3, 4]))
+    assert len(set(ix_samples_full)) == len(ix_samples_full)
+
+    # Test with frac_subsamples < 1.0
+    ix_samples_partial = get_random_sample_ids(X, rng, 0.5)
+    assert len(ix_samples_partial) == int(0.5 * len(X))
+    assert all(i in np.arange(len(X)) for i in ix_samples_partial)
+    assert len(set(ix_samples_partial)) == len(ix_samples_partial)
+
+
+def test_get_random_feature_ids():
+    X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
+    rng = np.random.RandomState(42)
+
+    # Test with frac_features = 1.0
+    ix_features_full = get_random_feature_ids(X, rng, 1.0)
+    assert np.array_equal(ix_features_full, np.array([0, 1, 2]))
+    assert len(set(ix_features_full)) == len(ix_features_full)
+
+    # Test with frac_features < 1.0
+    ix_features_partial = get_random_feature_ids(X, rng, 0.5)
+    assert len(ix_features_partial) == int(X.shape[1] * 0.5)
+    assert all(i in np.arange(X.shape[1]) for i in ix_features_partial)
+    assert len(set(ix_features_partial)) == len(ix_features_partial)
diff --git a/tests/decisiontree/test_split.py b/tests/models/decisiontree/test_split.py
similarity index 99%
rename from tests/decisiontree/test_split.py
rename to tests/models/decisiontree/test_split.py
index 6c2653a..6c3c0a2 100644
--- a/tests/decisiontree/test_split.py
+++ b/tests/models/decisiontree/test_split.py
@@ -5,8 +5,8 @@
 from pydantic import ValidationError
 from scipy import stats
 
-from random_tree_models.decisiontree.node import Node
-from random_tree_models.decisiontree.split import (
+from random_tree_models.models.decisiontree.node import Node
+from random_tree_models.models.decisiontree.split import (
     BestSplit,
     check_if_split_sensible,
     find_best_split,
@@ -15,7 +15,7 @@
     select_arrays_for_child_node,
     select_thresholds,
 )
-from random_tree_models.decisiontree.split_objects import SplitScore
+from random_tree_models.models.decisiontree.split_objects import SplitScore
 from random_tree_models.params import (
     ColumnSelectionMethod,
     ColumnSelectionParameters,
diff --git a/tests/decisiontree/test_split_objects.py b/tests/models/decisiontree/test_split_objects.py
similarity index 93%
rename from tests/decisiontree/test_split_objects.py
rename to tests/models/decisiontree/test_split_objects.py
index ee0f712..9aecd91 100644
--- a/tests/decisiontree/test_split_objects.py
+++ b/tests/models/decisiontree/test_split_objects.py
@@ -1,7 +1,7 @@
 import pytest
 from pydantic import ValidationError
 
-from random_tree_models.decisiontree.split_objects import SplitScore
+from random_tree_models.models.decisiontree.split_objects import SplitScore
 
 from .conftest import FLOAT_OPTIONS_NONE_OKAY, STR_OPTIONS_NONE_NOT_OKAY
 
diff --git a/tests/decisiontree/test_train.py b/tests/models/decisiontree/test_train.py
similarity index 93%
rename from tests/decisiontree/test_train.py
rename to tests/models/decisiontree/test_train.py
index ef5fb5b..debb309 100644
--- a/tests/decisiontree/test_train.py
+++ b/tests/models/decisiontree/test_train.py
@@ -3,9 +3,9 @@
 from dirty_equals import IsApprox
 from inline_snapshot import snapshot
 
-from random_tree_models.decisiontree.node import Node
-from random_tree_models.decisiontree.split_objects import SplitScore
-from random_tree_models.decisiontree.train import (
+from random_tree_models.models.decisiontree.node import Node
+from random_tree_models.models.decisiontree.split_objects import SplitScore
+from random_tree_models.models.decisiontree.train import (
     calc_leaf_weight_and_split_score,
     check_is_baselevel,
     grow_tree,
diff --git a/tests/decisiontree/test_visualize.py b/tests/models/decisiontree/test_visualize.py
similarity index 87%
rename from tests/decisiontree/test_visualize.py
rename to tests/models/decisiontree/test_visualize.py
index b77dda6..c285d6a 100644
--- a/tests/decisiontree/test_visualize.py
+++ b/tests/models/decisiontree/test_visualize.py
@@ -2,9 +2,9 @@
 from inline_snapshot import snapshot
 from pytest import CaptureFixture
 
-from random_tree_models.decisiontree.estimators import DecisionTreeTemplate
-from random_tree_models.decisiontree.node import Node
-from random_tree_models.decisiontree.visualize import show_tree
+from random_tree_models.models.decisiontree.estimators import DecisionTreeTemplate
+from random_tree_models.models.decisiontree.node import Node
+from random_tree_models.models.decisiontree.visualize import show_tree
 from random_tree_models.params import MetricNames
 
 
diff --git a/tests/test_extratrees.py b/tests/models/test_extratrees.py
similarity index 96%
rename from tests/test_extratrees.py
rename to tests/models/test_extratrees.py
index c35a9c5..e33401b 100644
--- a/tests/test_extratrees.py
+++ b/tests/models/test_extratrees.py
@@ -2,8 +2,8 @@
 import pytest
 from sklearn.utils.estimator_checks import parametrize_with_checks
 
-import random_tree_models.extratrees as et
-from random_tree_models.decisiontree import (
+import random_tree_models.models.extratrees as et
+from random_tree_models.models.decisiontree import (
     DecisionTreeClassifier,
     DecisionTreeRegressor,
 )
diff --git a/tests/test_gradientboostedtrees.py b/tests/models/test_gradientboostedtrees.py
similarity index 95%
rename from tests/test_gradientboostedtrees.py
rename to tests/models/test_gradientboostedtrees.py
index d939b7b..3c3f761 100644
--- a/tests/test_gradientboostedtrees.py
+++ b/tests/models/test_gradientboostedtrees.py
@@ -2,8 +2,8 @@
 import pytest
 from sklearn.utils.estimator_checks import parametrize_with_checks
 
-import random_tree_models.gradientboostedtrees as gbt
-from random_tree_models.decisiontree import (
+import random_tree_models.models.gradientboostedtrees as gbt
+from random_tree_models.models.decisiontree import (
     DecisionTreeRegressor,
 )
 from tests.conftest import expected_failed_checks
diff --git a/tests/test_isolationforest.py b/tests/models/test_isolationforest.py
similarity index 97%
rename from tests/test_isolationforest.py
rename to tests/models/test_isolationforest.py
index c127bc0..c3be640 100644
--- a/tests/test_isolationforest.py
+++ b/tests/models/test_isolationforest.py
@@ -1,6 +1,6 @@
 import numpy as np
 
-import random_tree_models.isolationforest as iforest
+import random_tree_models.models.isolationforest as iforest
 from random_tree_models.params import ThresholdSelectionMethod
 
 rng = np.random.RandomState(42)
diff --git a/tests/test_randomforest.py b/tests/models/test_randomforest.py
similarity index 95%
rename from tests/test_randomforest.py
rename to tests/models/test_randomforest.py
index ce377d5..840a96e 100644
--- a/tests/test_randomforest.py
+++ b/tests/models/test_randomforest.py
@@ -2,8 +2,8 @@
 import pytest
 from sklearn.utils.estimator_checks import parametrize_with_checks
 
-import random_tree_models.randomforest as rf
-from random_tree_models.decisiontree import (
+import random_tree_models.models.randomforest as rf
+from random_tree_models.models.decisiontree import (
     DecisionTreeClassifier,
     DecisionTreeRegressor,
 )
diff --git a/tests/test_xgboost.py b/tests/models/test_xgboost.py
similarity index 97%
rename from tests/test_xgboost.py
rename to tests/models/test_xgboost.py
index 188a7f2..83a84c1 100644
--- a/tests/test_xgboost.py
+++ b/tests/models/test_xgboost.py
@@ -2,8 +2,8 @@
 import pytest
 from sklearn.utils.estimator_checks import parametrize_with_checks
 
-import random_tree_models.xgboost as xgboost
-from random_tree_models.decisiontree import DecisionTreeRegressor
+import random_tree_models.models.xgboost as xgboost
+from random_tree_models.models.decisiontree import DecisionTreeRegressor
 from tests.conftest import expected_failed_checks
 
 
diff --git a/tests/test_params.py b/tests/test_params.py
index 547cdc8..ef530c0 100644
--- a/tests/test_params.py
+++ b/tests/test_params.py
@@ -7,6 +7,10 @@
     ThresholdSelectionMethod,
     ThresholdSelectionParameters,
     TreeGrowthParameters,
+    is_fraction,
+    is_greater_equal_zero,
+    is_greater_zero,
+    is_quantile,
 )
 
 
@@ -214,3 +218,97 @@ def test_frac_features(self, frac_features: float, fail: bool):
     def test_fail_if_max_depth_missing(self):
         with pytest.raises(ValidationError):
             _ = TreeGrowthParameters()  # type: ignore
+
+
+@pytest.mark.parametrize(
+    "q, ok",
+    [
+        (0.1, True),
+        (0.2, True),
+        (0.3, True),
+        (-0.1, False),
+        (1.1, False),
+        (0.4, False),
+        (0.51, False),
+    ],
+)
+def test_is_quantile(q: float, ok: bool):
+    try:
+        res = is_quantile(q)
+    except ValueError:
+        pass
+    else:
+        if ok:
+            assert res == q
+        else:
+            raise
+
+
+@pytest.mark.parametrize(
+    "f, ok",
+    [
+        (0.1, True),
+        (0.2, True),
+        (0.3, True),
+        (0.5, True),
+        (0.6, True),
+        (0.8, True),
+        (0.99, True),
+        (1.0, True),
+        (-0.1, False),
+        (1.1, False),
+        (0, False),
+    ],
+)
+def test_is_fraction(f: float, ok: bool):
+    try:
+        res = is_fraction(f)
+    except ValueError:
+        pass
+    else:
+        if ok:
+            assert res == f
+        else:
+            raise
+
+
+@pytest.mark.parametrize(
+    "v, ok",
+    [
+        (0.1, True),
+        (1.0, True),
+        (-0.1, False),
+        (0, False),
+    ],
+)
+def test_is_greater_zero(v: int, ok: bool):
+    try:
+        res = is_greater_zero(v)
+    except ValueError:
+        pass
+    else:
+        if ok:
+            assert res == v
+        else:
+            raise
+
+
+@pytest.mark.parametrize(
+    "v, ok",
+    [
+        (0.1, True),
+        (1.0, True),
+        (-0.1, False),
+        (0, True),
+    ],
+)
+def test_is_greater_equal_zero(v: float, ok: bool):
+    try:
+        res = is_greater_equal_zero(v)
+    except ValueError:
+        pass
+    else:
+        if ok:
+            assert res == v
+        else:
+            raise
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 7c852fa..2a11693 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,5 +1,6 @@
 import logging
 
+import numpy as np
 import pytest
 
 import random_tree_models.utils
@@ -36,3 +37,17 @@ def test_bool_to_float(x, exp, is_bad: bool):
         if is_bad:
             pytest.fail(f"Passed unexpectedly for non-bool value {x} returning {res}")
         assert res == exp
+
+
+def test_vectorize_bool_to_float():
+    y = np.array([True, False, True, False])
+    res = utils.vectorize_bool_to_float(y)
+    assert np.all(res == np.array([1.0, -1.0, 1.0, -1.0]))
+
+    y = np.array([True, False, True, True])
+    res = utils.vectorize_bool_to_float(y)
+    assert np.all(res == np.array([1.0, -1.0, 1.0, 1.0]))
+
+    y = np.array([False, False, True, False])
+    res = utils.vectorize_bool_to_float(y)
+    assert np.all(res == np.array([-1.0, -1.0, 1.0, -1.0]))