no_change

Linear_Lasso_Regression.ipynb

@@ -623,7 +623,7 @@
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.PairGrid at 0x1fba68ade40>"
+       "<seaborn.axisgrid.PairGrid at 0x1e4f42723b0>"
       ]
      },
      "execution_count": 10,
@@ -1027,8 +1027,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.13370217 0.09055658 0.18901264 0.15308789 0.29435119 0.09109181\n",
-      " 0.04819774]\n"
+      "[0.14383604 0.08356084 0.20643927 0.14923726 0.27764975 0.09325222\n",
+      " 0.04602462]\n"
      ]
     }
    ],
@@ -1043,7 +1043,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1070,7 +1070,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\805ka\\AppData\\Local\\Temp\\ipykernel_5364\\476133659.py:1: UserWarning: \n",
+      "C:\\Users\\805ka\\AppData\\Local\\Temp\\ipykernel_10300\\476133659.py:1: UserWarning: \n",
       "\n",
       "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
       "\n",
@@ -1382,7 +1382,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\805ka\\AppData\\Local\\Temp\\ipykernel_5364\\1520944062.py:1: UserWarning: \n",
+      "C:\\Users\\805ka\\AppData\\Local\\Temp\\ipykernel_10300\\1520944062.py:1: UserWarning: \n",
       "\n",
       "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
       "\n",
@@ -1430,7 +1430,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x1fbae7474f0>"
+       "<matplotlib.collections.PathCollection at 0x1e4f9f46950>"
       ]
      },
      "execution_count": 30,
@@ -1780,7 +1780,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\805ka\\AppData\\Local\\Temp\\ipykernel_5364\\1520944062.py:1: UserWarning: \n",
+      "C:\\Users\\805ka\\AppData\\Local\\Temp\\ipykernel_10300\\1520944062.py:1: UserWarning: \n",
       "\n",
       "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
       "\n",
@@ -1828,7 +1828,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x1fbae90d2d0>"
+       "<matplotlib.collections.PathCollection at 0x1e4fa03f820>"
       ]
      },
      "execution_count": 44,