Tutrioum 3
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "db3dfd7e-3faf-401d-ac43-f888f445563a",
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"metadata": {},
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"outputs": [],
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"source": [
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"#pip install --upgrade pip"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "c5222c9f-320f-42ac-a20c-46b235e14116",
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"metadata": {},
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"outputs": [],
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"source": [
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"#pip install scikit-learn"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "a82b5fcc-1eed-45bc-9ac3-65242a42acc4",
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"metadata": {},
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"outputs": [],
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"source": [
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"#pip install statsmodels"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "9665eb0a-82a0-4b38-8279-e1ce4b85dfa3",
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"metadata": {},
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"outputs": [],
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"source": [
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"import itertools\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn.datasets import load_iris\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from sklearn.linear_model import LinearRegression\n",
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"from statsmodels.graphics.tsaplots import plot_acf\n",
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"import statsmodels.api as sm\n",
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"import scipy.stats as stats\n",
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"from sklearn.model_selection import KFold\n",
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"from sklearn.metrics import mean_squared_error"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "50007140-4393-4fdd-a774-1ec4b8db9ca6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load iris dataset\n",
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"data = load_iris()\n",
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"X_full = pd.DataFrame(data.data, columns=data.feature_names)\n",
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"y = pd.Series(data.target) # Species as numeric labels (0, 1, 2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "667d2530-3d5f-439f-80ed-b1b07902629d",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
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"text/plain": [
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"<Figure size 600x400 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 600x400 with 0 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<Figure size 600x400 with 0 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAHWCAYAAAD6oMSKAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvc2/+5QAAAAlwSFlzAAAPYQAAD2EBqD+naQAAdORJREFUeJzt3Xd8jef/x/HXyRYypBIJYqvae2+CVKvVXaNGtRQprQ6UClVV3y61VVuqSwelOmKPUjvUpohREitNQpDEOffvj/PLqciQE4ms9/PxyOPrvs99X/fnyO/3/b5d131dl8kwDAMRERERyfcccrsAEREREckeCnYiIiIiBYSCnYiIiEgBoWAnIiIiUkAo2ImIiIgUEAp2IiIiIgWEgp2IiIhIAaFgJyIiIlJAKNiJiIiIFBAKdiJSoJhMJsaNG5fbZaSwfft2mjdvTtGiRTGZTOzevTu3S0ph/vz5mEwmTpw4cdtry5cvT9++fXO0nr59+1K+fPkcfYZIQaVgJyKp7N+/n169elG6dGlcXV0pVaoUvXr14sCBA7nS3okTJzCZTLYfR0dHypYtyyOPPJJtIenAgQOMGzcuU+HGHklJSTzxxBNER0fz0Ucf8eWXX1KuXLk0r123bl2q7+nn58fjjz/OwYMHs7UuESmYnHK7ABHJWxYvXkz37t3x8fGhf//+VKhQgRMnTvDZZ5/x448/8t133/Hwww/nSnvdu3enS5cumM1mDh48yKxZs/j999/ZsmULdevWzeI3tjpw4ADjx4+nbdu22dpbdOzYMU6ePMncuXN57rnnMnXP0KFDadSoEUlJSezZs4fZs2ezbt069u3bh7+/f7bVluyZZ57h6aefxtXVNdvbFpG7S8FORGyOHTvGM888Q8WKFdmwYQO+vr62z4YNG0arVq3o1asXe/bsoUKFCne9vfr169OrVy/bcYsWLXjooYeYNWsWc+bMsfPb3h3nz58HwNvbO9P3tGrViscff9x2XLVqVQYNGsSCBQt4/fXXs7tEHB0dcXR0zPZ2ReTu01CsiNi89957XL16lU8++SRFCAMoUaIEc+bM4cqVK7z33nu50t6t2rdvD0BERESG1+3atYv7778fT09PihUrRocOHdiyZYvt8/nz5/PEE08A0K5dO9tQ6Lp16zJsd82aNbRq1YqiRYvi7e3Nww8/nGLItG/fvrRp0waAJ554ApPJRNu2be3+nq1atQKsQflmZ86c4dlnn6VkyZK4urpSo0YNPv/881T3T5s2jRo1auDu7k7x4sVp2LAh33zzTYrvf+s7doZh8Pbbb1OmTBnc3d1p164d+/fvT9X2uHHjMJlMqc6n1ebSpUt54IEHKFWqFK6urlSqVIkJEyZgNptv+3ewcOFCGjRogIeHB56entSqVYuPP/74tveJFDbqsRMRm2XLllG+fHlbkLhV69atKV++PMuWLWPmzJl3vb1bJQede+65J91r9u/fT6tWrfD09OT111/H2dmZOXPm0LZtW9avX0+TJk1o3bo1Q4cOZerUqbzxxhtUq1YNwPafaVm1ahX3338/FStWZNy4cVy7do1p06bRokULwsPDKV++PAMHDqR06dK88847tuHVkiVL2v09k8NR8eLFbefOnTtH06ZNMZlMhISE4Ovry++//07//v2Ji4vjpZdeAmDu3LkMHTqUxx9/nGHDhnH9+nX27NnD1q1b6dGjR7rPHDt2LG+//TZdunShS5cuhIeH06lTJxITE+2uP9n8+fMpVqwYw4cPp1ixYqxZs4axY8cSFxeXYbhfuXIl3bt3p0OHDkyePBmAgwcPsmnTJoYNG5blekQKJENExDCMmJgYAzAefvjhDK976KGHDMCIi4u7a+1FREQYgDF+/HjjwoULRlRUlLFu3TqjXr16BmAsWrTIdi1ghIaG2o67detmuLi4GMeOHbOdO3v2rOHh4WG0bt3adu6HH34wAGPt2rUZ1pusbt26hp+fn3Hp0iXbub/++stwcHAwevfubTu3du1aAzB++OGH27aZfO3nn39uXLhwwTh79qwRFhZmVK5c2TCZTMa2bdts1/bv398ICAgwLl68mKKNp59+2vDy8jKuXr1qGIZhPPzww0aNGjUyfO68efMMwIiIiDAMwzDOnz9vuLi4GA888IBhsVhs173xxhsGYPTp08d2LjQ01Ejrf0pubdMwDFtNNxs4cKDh7u5uXL9+3XauT58+Rrly5WzHw4YNMzw9PY0bN25k+D1ExDA0FCsiAFy+fBkADw+PDK9L/jz5+rvVHkBoaCi+vr74+/vTtm1bjh07xuTJk3n00UfTvN5sNrNixQq6detGxYoVbecDAgLo0aMHGzduJC4u7rbPvVVkZCS7d++mb9+++Pj42M7Xrl2bjh078ttvv9nd5s2effZZfH19KVWqFMHBwcTGxvLll1/SqFEjwDpMumjRIrp27YphGFy8eNH207lzZ2JjYwkPDwes7/b9888/bN++PdPPX7VqFYmJibz44osphlmTewGzqkiRIrY/X758mYsXL9KqVSuuXr3KoUOH0r3P29ub+Ph4Vq5ceUfPFykMNBQrIoB9gc1kMlGiRAkAoqOjUwzPFSlSBC8vryy3l5EBAwbwxBNP4ODggLe3NzVq1MhwJueFCxe4evUqVatWTfVZtWrVsFgsnD59mho1atz22Tc7efIkQLrtLl++nPj4eIoWLWpXu8nGjh1Lq1atuHLlCj/99BMLFy7EweG/f4dfuHCBmJgYPvnkEz755JM020ietDFixAhWrVpF48aNqVy5Mp06daJHjx60aNHitt+vSpUqKc77+vqmGA621/79+xkzZgxr1qxJFahjY2PTvW/w4MF8//333H///ZQuXZpOnTrx5JNPEhwcnOVaRAoqBTsRAcDLy4tSpUqxZ8+eDK/bs2cPZcqUwcXFBYBHH32U9evX2z7v06cP8+fPz3J7GalSpQpBQUGZ+Db5W61atWzfs1u3bly9epXnn3+eli1bEhgYiMViAaBXr1706dMnzTZq164NWIPm4cOH+eWXXwgLC2PRokXMnDmTsWPHMn78+DuuNa2JE0CqCRExMTG0adMGT09P3nrrLSpVqoSbmxvh4eGMGDHC9p3S4ufnx+7du1m+fDm///47v//+O/PmzaN379588cUXd/wdRAoSBTsRsenatStz5sxh48aNtGzZMtXnf/zxBydOnGD48OG2cx988AH//vuv7bhUqVJ31F528vX1xd3dncOHD6f67NChQzg4OBAYGAikH1DSkrzAcHrtlihRIsu9dWl59913+emnn5g4cSKzZ8/G19cXDw8PzGZzpoJu0aJFeeqpp3jqqadITEzk0UcfZeLEiYwaNQo3N7dU1yd/v7///jvFEPaFCxdS/K7hvwkdMTExKZZ0Se71S7Zu3TouXbrE4sWLad26te387WY0J3NxcaFr16507doVi8XC4MGDmTNnDm+++SaVK1fOVBsihYHesRMRm1dffRV3d3cGDhzIpUuXUnwWHR3NCy+8gKenJyEhIbbzDRo0ICgoyPZTvXr1O2ovOzk6OtKpUyeWLl2aYtmNc+fO8c0339CyZUs8PT0BbEEsJibmtu0GBARQt25dvvjiixTX79u3jxUrVtClS5fs/BpUqlSJxx57jPnz5xMVFYWjoyOPPfYYixYtYt++famuv3Dhgu3Pt/69u7i4UL16dQzDICkpKc3nBQUF4ezszLRp0zAMw3Z+ypQpadYGsGHDBtu5+Pj4VD1pyevk3dxeYmJipmZD3/odHBwcbD2SCQkJt71fpDBRj52I2FSuXJkFCxbQvXt3atWqlWqniH///ZeFCxdmajHhnGgvK95++21WrlxJy5YtGTx4ME5OTsyZM4eEhAT+97//2a6rW7cujo6OTJ48mdjYWFxdXWnfvj1+fn5ptvvee+9x//3306xZM/r3729b7sTLyytH9qp97bXX+P7775kyZQrvvvsu7777LmvXrqVJkyY8//zzVK9enejoaMLDw1m1ahXR0dEAdOrUCX9/f1q0aEHJkiU5ePAg06dP54EHHkh3Youvry+vvvoqkyZN4sEHH6RLly7s2rWL33//PdW7kJ06daJs2bL079+f1157DUdHRz7//HN8fX05deqU7brmzZtTvHhx+vTpw9ChQzGZTHz55Zcpgl56nnvuOaKjo2nfvj1lypTh5MmTTJs2jbp162a4JI1IoZSbU3JFJG/au3ev0aNHD8Pf399wcHAwAMPNzc3Yv39/rrSXvNzJe++9d9truWW5E8MwjPDwcKNz585GsWLFDHd3d6Ndu3bGn3/+mereuXPnGhUrVjQcHR0ztfTJqlWrjBYtWhhFihQxPD09ja5duxoHDhxIcU1WljtJ79q2bdsanp6eRkxMjGEYhnHu3DljyJAhRmBgoOHs7Gz4+/sbHTp0MD755BPbPXPmzDFat25t3HPPPYarq6tRqVIl47XXXjNiY2Nt16S1NInZbDbGjx9vBAQEGEWKFDHatm1r7Nu3zyhXrlyK5U4MwzB27txpNGnSxHBxcTHKli1rfPjhh2m2uWnTJqNp06ZGkSJFjFKlShmvv/66sXz58lR/17cud/Ljjz8anTp1Mvz8/GzPGDhwoBEZGXnbv1ORwsZkGJn455KIFGoLFiygb9++9OrViwULFuS59kRExEpDsSJyW7179yYyMpKRI0dSpkwZ3nnnnTzVnoiIWKnHTkRERKSA0KxYERERkQJCwU5ERESkgFCwExERESkg8l2wmzFjBuXLl8fNzY0mTZqwbdu2DK+PiYlhyJAhBAQE4Orqyr333nvHG3SLiIiI5EX5albsd999x/Dhw5k9ezZNmjRhypQpdO7cmcOHD6e5iGhiYiIdO3bEz8+PH3/8kdKlS3Py5MkU297cjsVi4ezZs3h4eNi15ZCIiIhIdjAMg8uXL1OqVCkcHG7TJ5erq+jZqXHjxsaQIUNsx2az2ShVqpQxadKkNK+Line truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Fit linear regression model for the full model\n",
|
||||
"model = LinearRegression()\n",
|
||||
"model.fit(X_full, y)\n",
|
||||
"y_pred = model.predict(X_full)\n",
|
||||
"residuals = y - y_pred\n",
|
||||
"\n",
|
||||
"# Residuals vs Fitted\n",
|
||||
"plt.figure(figsize=(6, 4))\n",
|
||||
"sns.scatterplot(x=y_pred, y=residuals)\n",
|
||||
"plt.axhline(0, color='red', linestyle='--')\n",
|
||||
"plt.title(\"Residuals vs Fitted Values\")\n",
|
||||
"plt.xlabel(\"Fitted Values\")\n",
|
||||
"plt.ylabel(\"Residuals\")\n",
|
||||
"plt.tight_layout()\n",
|
||||
"plt.show()\n",
|
||||
"\n",
|
||||
"# Autocorrelation of residuals\n",
|
||||
"plt.figure(figsize=(6, 4))\n",
|
||||
"plot_acf(residuals, lags=20)\n",
|
||||
"plt.title(\"Autocorrelation of Residuals\")\n",
|
||||
"plt.tight_layout()\n",
|
||||
"plt.show()\n",
|
||||
"\n",
|
||||
"# Q-Q Plot for residuals\n",
|
||||
"plt.figure(figsize=(6, 4))\n",
|
||||
"sm.qqplot(residuals, line='s')\n",
|
||||
"plt.title(\"Q-Q Plot of Residuals\")\n",
|
||||
"plt.tight_layout()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "2b24c560-c4f2-4dc8-b570-e6430df3d93b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# All non-empty combinations of the features\n",
|
||||
"feature_names = X_full.columns.tolist()\n",
|
||||
"combinations = []\n",
|
||||
"for r in range(1, len(feature_names) + 1):\n",
|
||||
" combinations.extend(itertools.combinations(feature_names, r))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "574e9024-fbef-4c75-9bec-9652591651c9",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"results = []"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "a3ad39f4-c1da-4f36-ac8c-14190bf3dfe4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Cross-validation setup\n",
|
||||
"kf = KFold(n_splits=5, shuffle=True, random_state=42)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "a819a2c6-033a-4a63-92b1-d97c10ff6967",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# BIC calculation helper\n",
|
||||
"def compute_bic(n, mse, k):\n",
|
||||
" return n * np.log(mse) + k * np.log(n)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "effbc2b4-197a-4a51-9643-154a60417dc9",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Try all combinations\n",
|
||||
"for combo in combinations:\n",
|
||||
" X = X_full[list(combo)]\n",
|
||||
" bic_scores = []\n",
|
||||
" coefs = []\n",
|
||||
" \n",
|
||||
" for train_idx, test_idx in kf.split(X):\n",
|
||||
" X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n",
|
||||
" y_train, y_test = y.iloc[train_idx], y.iloc[test_idx]\n",
|
||||
" \n",
|
||||
" model = LinearRegression()\n",
|
||||
" model.fit(X_train, y_train)\n",
|
||||
" y_pred = model.predict(X_test)\n",
|
||||
" mse = mean_squared_error(y_test, y_pred)\n",
|
||||
" \n",
|
||||
" n = len(y_test)\n",
|
||||
" k = X.shape[1] + 1 # Number of parameters (coefficients + intercept)\n",
|
||||
" bic = compute_bic(n, mse, k)\n",
|
||||
" bic_scores.append(bic)\n",
|
||||
" coefs.append(model.coef_)\n",
|
||||
" \n",
|
||||
" avg_bic = np.mean(bic_scores)\n",
|
||||
" avg_coef = np.mean(coefs, axis=0)\n",
|
||||
" \n",
|
||||
" results.append({\n",
|
||||
" \"Features\": combo,\n",
|
||||
" \"BIC\": avg_bic,\n",
|
||||
" \"Coefficients\": avg_coef\n",
|
||||
" })"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "22f12490-8a71-4d5f-abc9-e739045e63df",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" Features BIC Coefficients\n",
|
||||
" petal length (cm), petal width (cm) -79.966119 [0.17796320237066632, 0.6310854591966504]\n",
|
||||
" petal width (cm) -79.935376 [1.028084679087288]\n",
|
||||
" sepal width (cm), petal width (cm) -78.272210 [-0.16548923827800194, 0.993352500599156]\n",
|
||||
" sepal length (cm), petal length (cm), petal width (cm) -78.159707 [-0.13614298020063995, 0.25187698514269174, 0.5870393766101574]\n",
|
||||
" sepal width (cm), petal length (cm), petal width (cm) -77.208867 [-0.11236946402530915, 0.14877065541738183, 0.672522147599077]\n",
|
||||
" sepal length (cm), petal width (cm) -76.449874 [0.0004422302955151648, 1.0276993666680874]\n",
|
||||
" sepal length (cm), sepal width (cm), petal width (cm) -74.986676 [0.05489849537791543, -0.18729534023952985, 0.9400712879485124]\n",
|
||||
" petal length (cm) -74.609844 [0.4404275685811787]\n",
|
||||
"sepal length (cm), sepal width (cm), petal length (cm), petal width (cm) -74.050455 [-0.11132652022667129, -0.039417808876468105, 0.22805675504036355, 0.6096716323413338]\n",
|
||||
" sepal length (cm), petal length (cm) -73.855114 [-0.18379890365847038, 0.5154692946597005]\n",
|
||||
" sepal width (cm), petal length (cm) -71.011363 [-0.04516426811005535, 0.43549330263873876]\n",
|
||||
" sepal length (cm), sepal width (cm), petal length (cm) -70.426945 [-0.2369466563239726, 0.09625805574290999, 0.5472156556436609]\n",
|
||||
" sepal length (cm), sepal width (cm) -41.087834 [0.7352514155349378, -0.635832492759423]\n",
|
||||
" sepal length (cm) -33.713489 [0.7745557259258307]\n",
|
||||
" sepal width (cm) -11.311518 [-0.7970243145470655]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Convert results to DataFrame and sort by BIC\n",
|
||||
"df_results = pd.DataFrame(results)\n",
|
||||
"df_results[\"Features\"] = df_results[\"Features\"].apply(lambda x: \", \".join(x))\n",
|
||||
"df_results = df_results.sort_values(by=\"BIC\")\n",
|
||||
"\n",
|
||||
"# Show results\n",
|
||||
"print(df_results.to_string(index=False))# Find and print the best model based on BIC"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "937e43b6-9d9a-40e3-b48e-2af781e0488c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"Best Model Based on BIC:\n",
|
||||
"Features : petal length (cm), petal width (cm)\n",
|
||||
"BIC : -79.97\n",
|
||||
"Coefficients : [0.1779632 0.63108546]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Find and print the best model based on BIC\n",
|
||||
"best_model = df_results.iloc[0]\n",
|
||||
"\n",
|
||||
"print(\"\\nBest Model Based on BIC:\")\n",
|
||||
"print(f\"Features : {best_model['Features']}\")\n",
|
||||
"print(f\"BIC : {best_model['BIC']:.2f}\")\n",
|
||||
"print(f\"Coefficients : {best_model['Coefficients']}\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -0,0 +1,216 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "fda1553d-3a8e-4d0c-a693-4f8040cbb4ec",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import seaborn as sns\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"from sklearn.datasets import load_iris"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "3b0a78ff-c2bd-406d-979c-1ffe1fd4fffb",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Load dataset\n",
|
||||
"iris = load_iris()\n",
|
||||
"df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
|
||||
"df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "4a1b1bc5-a609-4572-8e7d-4d512c848673",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1117.75x1000 with 20 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Pairplot\n",
|
||||
"sns.pairplot(df, hue='species', diag_kind='kde')\n",
|
||||
"plt.suptitle(\"Pairplot of Iris Features\", y=1.02)\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "1fed0af8-9973-4c87-ac8b-d8b81389b037",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 600x400 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
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||||
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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 600x400 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 600x400 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 600x400 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Boxplots for each feature by species\n",
|
||||
"for feature in iris.feature_names:\n",
|
||||
" plt.figure(figsize=(6, 4))\n",
|
||||
" sns.boxplot(data=df, x='species', y=feature)\n",
|
||||
" plt.title(f'Boxplot of {feature} by Species')\n",
|
||||
" plt.tight_layout()\n",
|
||||
" plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "c983e8b5-8ab8-45f8-9b7a-7b8a7210631f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 600x500 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Correlation heatmap\n",
|
||||
"plt.figure(figsize=(6, 5))\n",
|
||||
"corr = df.drop(columns='species').corr()\n",
|
||||
"sns.heatmap(corr, annot=True, cmap='coolwarm', square=True)\n",
|
||||
"plt.title(\"Feature Correlation Heatmap\")\n",
|
||||
"plt.tight_layout()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "16a90a2e-9177-460d-a3a9-8c22553a9557",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 600x400 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"text/plain": [
|
||||
"<Figure size 600x400 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"text/plain": [
|
||||
"<Figure size 600x400 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 600x400 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Violin plots for each feature by species\n",
|
||||
"for feature in iris.feature_names:\n",
|
||||
" plt.figure(figsize=(6, 4))\n",
|
||||
" sns.violinplot(data=df, x='species', y=feature, inner=\"box\")\n",
|
||||
" plt.title(f'Violin Plot of {feature} by Species')\n",
|
||||
" plt.tight_layout()\n",
|
||||
" plt.show()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Reference in new issue
Block a user