class CART init and fit

decision_tree/cart.ipynb

@@ -137,6 +137,30 @@
     "y = np.array([0, 1, 1, 0])\n",
     "print(gini_split(X, y, feature_index, threshold))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class CART():\n",
+    "    def __init__(\n",
+    "            self,\n",
+    "            max_depth=3,\n",
+    "            min_samples_split=2,\n",
+    "            criterion='gini',\n",
+    "            task_type='classification'\n",
+    "        ):\n",
+    "        self.max_depth = max_depth\n",
+    "        self.min_samples_split = min_samples_split\n",
+    "        self.criterion = criterion\n",
+    "        self.task_type = task_type\n",
+    "        self.tree = None\n",
+    "\n",
+    "    def fit(self, X, y):\n",
+    "        self.tree = self._build_tree(X, y)"
+   ]
   }
  ],
  "metadata": {