scikit-learn
diff --git a/‎sklearn/tests/test_public_functions.py
Copy file name to clipboardExpand all lines: sklearn/tests/test_public_functions.py
+1Lines changed: 1 addition & 0 deletions b/‎sklearn/tests/test_public_functions.py
Copy file name to clipboardExpand all lines: sklearn/tests/test_public_functions.py
+1Lines changed: 1 addition & 0 deletions
diff --git a/‎sklearn/tree/_export.py
Copy file name to clipboardExpand all lines: sklearn/tree/_export.py
+18-15Lines changed: 18 additions & 15 deletions b/‎sklearn/tree/_export.py
Copy file name to clipboardExpand all lines: sklearn/tree/_export.py
+18-15Lines changed: 18 additions & 15 deletions
@@ -194,6 +194,7 @@ def _check_function_param_validation(
     "sklearn.random_projection.johnson_lindenstrauss_min_dim",
     "sklearn.svm.l1_min_c",
     "sklearn.tree.export_text",
+    "sklearn.tree.plot_tree",
     "sklearn.utils.gen_batches",
 ]
 
 
@@ -17,7 +17,7 @@
 import numpy as np
 
 from ..utils.validation import check_is_fitted
-from ..utils._param_validation import Interval, validate_params
+from ..utils._param_validation import Interval, validate_params, StrOptions
 
 from ..base import is_classifier
 
@@ -77,6 +77,23 @@ def __repr__(self):
 SENTINEL = Sentinel()
 
 
+@validate_params(
+    {
+        "decision_tree": [DecisionTreeClassifier, DecisionTreeRegressor],
+        "max_depth": [Interval(Integral, 0, None, closed="left"), None],
+        "feature_names": [list, None],
+        "class_names": [list, None],
+        "label": [StrOptions({"all", "root", "none"})],
+        "filled": ["boolean"],
+        "impurity": ["boolean"],
+        "node_ids": ["boolean"],
+        "proportion": ["boolean"],
+        "rounded": ["boolean"],
+        "precision": [Interval(Integral, 0, None, closed="left"), None],
+        "ax": "no_validation",  # delegate validation to matplotlib
+        "fontsize": [Interval(Integral, 0, None, closed="left"), None],
+    }
+)
 def plot_tree(
     decision_tree,
     *,
@@ -601,20 +618,6 @@ def __init__(
         )
         self.fontsize = fontsize
 
-        # validate
-        if isinstance(precision, Integral):
-            if precision < 0:
-                raise ValueError(
-                    "'precision' should be greater or equal to 0."
-                    " Got {} instead.".format(precision)
-                )
-        else:
-            raise ValueError(
-                "'precision' should be an integer. Got {} instead.".format(
-                    type(precision)
-                )
-            )
-
         # The depth of each node for plotting with 'leaf' option
         self.ranks = {"leaves": []}
         # The colors to render each node with
Original file line number	Diff line number	Diff line change
`@@ -194,6 +194,7 @@ def _check_function_param_validation(`
`194`	`194`	`"sklearn.random_projection.johnson_lindenstrauss_min_dim",`
`195`	`195`	`"sklearn.svm.l1_min_c",`
`196`	`196`	`"sklearn.tree.export_text",`
	`197`	`+ "sklearn.tree.plot_tree",`
`197`	`198`	`"sklearn.utils.gen_batches",`
`198`	`199`	`]`
`199`	`200`