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Supervised_(Classification)_ML_Model_Training_and_Evulation_.ipynb

@@ -58781,6 +58781,31 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Notes**\n",
+        "\n",
+        "Multi-class Classification: If y contains multiple classes, LogisticRegression uses the one-vs-rest (OvR) or multinomial strategy depending on the solver.\n",
+        "\n",
+        "**Solvers:**\n",
+        "\n",
+        "liblinear: Good for small datasets; supports L1 and L2 regularization.\n",
+        "\n",
+        "lbfgs: Optimized for multiclass problems.\n",
+        "\n",
+        "saga: Handles large datasets; supports L1, L2, and elastic net regularization.\n",
+        "\n",
+        "**Hyperparameter Tuning:**\n",
+        "\n",
+        "penalty: Regularization type (l1, l2, elasticnet, none).\n",
+        "\n",
+        "C: Inverse of regularization strength (smaller values => stronger regularization)"
+      ],
+      "metadata": {
+        "id": "vs-htHpRU5eW"
+      }
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -59006,15 +59031,6 @@
       "execution_count": null,
       "outputs": []
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "IHXlJENZBfRD"
-      },
-      "source": [
-        "### **Model Tunning**"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {