fix issues

Author: xadupre

_doc/articles/2025/2025-03-01-route2025.rst

@@ -48,13 +48,17 @@ Séance 2 (7/2)
 ==============
 
 * arbre de régression, de classification
- :ref:`RandomForest, Overfitting <nbl-practice-ml-ml_a_tree_overfitting>`
+  :ref:`RandomForest, Overfitting <nbl-practice-ml-ml_a_tree_overfitting>`
 * Gradient Boosting, :ref:`Gradient Boosting et Learning Rate avec les Random Forest <nbl-practice-ml-gradient_boosting>`
 * Régression linéaire et contraintes sur les coefficients,
   `Ridge <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html>`_,
   `Lasso <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html>`_,
   `ElasticNet <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html>`_,
   :ref:`Ridge, Lasso, mathématiques <nbl-practice-ml-ridge_lasso>`
+* paramètres et hyper paramètres, :ref:`Sélection des hyper-paramètres <nbl-practice-ml-winesr_knn_hyper>`,
+  :class:`sklearn.model_selection.GridSearchCV`
+* `Data challenge - Algorithme machine learning qui permet de prédire la gravité d’un accident de la rout
+  <https://www.data.gouv.fr/fr/reuses/data-challenge-algorithme-machine-learning-qui-permet-de-predire-la-gravite-dun-accident-de-la-route/>`_
 
 Séance 3 (14/2)
 ===============

_doc/articles/2025/2025-04-01-route2025.rst

@@ -1,8 +1,8 @@
 .. _l-feuille-route-2025-2:
 
-==================================
-2025-04-01 : feuille de route 2025
-==================================
+==========================================
+2025-04-01 : feuille de route 2025 - avril
+==========================================
 
 site web : `sdpython.github.io <https://sdpython.github.io/>`_
 

_doc/practice/ml/gradient_boosting.ipynb

@@ -1438,11 +1438,11 @@
     "        color = \"b\"\n",
     "    else:\n",
     "        y_ = sum(\n",
-    "            [model.init_.predict(X_test)]\n",
-    "            + [\n",
+    "            [model.init_.predict(X_test),\n",
+    "            *[\n",
     "                model.estimators_[k, 0].predict(X_test) * model.learning_rate\n",
     "                for k in range(0, i)\n",
-    "            ]\n",
+    "            ]]\n",
     "        )\n",
     "        color = \"r\"\n",
     "    df = DataFrame({\"X\": X_test[ind].ravel(), \"y^\": y_[ind].ravel()})\n",
@@ -6111,4 +6111,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

teachpyx/practice/rues_paris.py

@@ -125,7 +125,7 @@ def get_data(
 
 
 def graph_degree(
-    edges: List[Tuple[int, int, int, Tuple[float, float], Tuple[float, float], float]]
+    edges: List[Tuple[int, int, int, Tuple[float, float], Tuple[float, float], float]],
 ) -> Dict[Tuple[int, int], int]:
     """
     Calcul le degré de chaque noeud.
@@ -367,7 +367,7 @@ def eulerien_extension(
 
 
 def connected_components(
-    edges: List[Tuple[int, int, int, Tuple[float, float], Tuple[float, float], float]]
+    edges: List[Tuple[int, int, int, Tuple[float, float], Tuple[float, float], float]],
 ) -> Dict[int, int]:
     """
     Computes the connected components.