fml

5/Labo5.ipynb

@@ -97,15 +97,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "392\n",
-      "392\n"
+      "<class 'sklearn.neighbors.classification.KNeighborsClassifier'>\n"
      ]
     },
     {
@@ -117,18 +116,6 @@
       "/home/beppe/.local/lib/python3.7/site-packages/sklearn/utils/validation.py:563: FutureWarning: Beginning in version 0.22, arrays of bytes/strings will be converted to decimal numbers if dtype='numeric'. It is recommended that you convert the array to a float dtype before using it in scikit-learn, for example by using your_array = your_array.astype(np.float64).\n",
       "  FutureWarning)\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
-       "           metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
-       "           weights='uniform')"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -160,7 +147,9 @@
     "xtrain, xtest, ytrain, ytest = train_test_split(setjen, target, random_state=0)\n",
     "\n",
     "knn = KNeighborsClassifier(n_neighbors=1)\n",
-    "knn.fit(xtrain, ytrain)\n"
+    "f = knn.fit(xtrain, ytrain)\n",
+    "print(type(f))\n",
+    "\n"
    ]
   },
   {