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‎cross validation.ipynb

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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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Import Library\n",
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"from numpy import array\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.model_selection import KFold"
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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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"# data sample\n",
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"data = pd.read_csv(\"~/Downloads/Data Science/data set/book.csv\")\n",
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"data = np.asarray(data)"
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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": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"train: [[0 1 0 ... 0 0 0]\n",
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" [1 0 0 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]\n",
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" ...\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 0 1 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]], test: [[1 0 0 ... 0 0 0]\n",
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" [1 1 1 ... 0 0 0]\n",
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" [1 0 1 ... 0 0 0]\n",
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" ...\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 1 0 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]]\n",
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"train: [[0 1 0 ... 0 0 0]\n",
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" [1 0 0 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]\n",
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" ...\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 0 1 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]], test: [[1 1 1 ... 0 0 0]\n",
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" [1 0 0 ... 0 0 0]\n",
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" [0 0 1 ... 0 0 0]\n",
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" ...\n",
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" [1 1 1 ... 1 1 0]\n",
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" [0 0 0 ... 0 0 0]\n",
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" [1 0 1 ... 0 0 0]]\n",
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"train: [[1 0 0 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]\n",
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" [1 1 1 ... 0 0 0]\n",
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" ...\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 0 1 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]], test: [[0 1 0 ... 0 0 0]\n",
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" [1 1 1 ... 0 0 1]\n",
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" [0 0 0 ... 0 0 0]\n",
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" ...\n",
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" [1 0 0 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]]\n",
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"train: [[0 1 0 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]\n",
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" [1 1 1 ... 0 0 0]\n",
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" ...\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 0 1 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]], test: [[1 0 0 ... 0 0 0]\n",
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" [1 0 0 ... 0 0 1]\n",
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" [0 0 1 ... 0 0 0]\n",
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" ...\n",
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" [1 0 0 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 0 1 ... 0 0 0]]\n",
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"train: [[0 1 0 ... 0 0 0]\n",
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" [1 0 0 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]\n",
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" ...\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 0 1 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]], test: [[0 1 0 ... 0 0 0]\n",
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" [1 0 1 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 1]\n",
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" ...\n",
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" [1 0 0 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 1 1 ... 0 1 0]]\n",
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"train: [[0 1 0 ... 0 0 0]\n",
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" [1 0 0 ... 0 0 0]\n",
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" [1 1 1 ... 0 0 0]\n",
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" ...\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 0 1 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]], test: [[0 0 0 ... 0 0 0]\n",
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" [1 0 0 ... 0 0 1]\n",
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" [1 1 1 ... 0 0 0]\n",
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" ...\n",
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" [1 1 1 ... 0 1 0]\n",
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" [1 0 0 ... 0 0 0]\n",
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" [1 0 0 ... 0 0 0]]\n",
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"train: [[0 1 0 ... 0 0 0]\n",
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" [1 0 0 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]\n",
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" ...\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 0 1 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]], test: [[0 0 1 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 0 1 ... 0 1 0]\n",
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" ...\n",
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" [0 1 1 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 0 1 ... 0 1 1]]\n",
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"train: [[0 1 0 ... 0 0 0]\n",
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" [1 0 0 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]\n",
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" ...\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 0 1 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]], test: [[0 0 1 ... 0 0 0]\n",
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" [0 1 0 ... 0 0 0]\n",
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" [1 1 1 ... 0 0 0]\n",
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" ...\n",
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" [0 0 1 ... 0 0 0]\n",
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" [1 1 1 ... 0 0 0]\n",
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" [1 0 0 ... 0 0 0]]\n",
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"train: [[0 1 0 ... 0 0 0]\n",
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" [1 0 0 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]\n",
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" ...\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 0 1 ... 0 0 0]], test: [[0 0 1 ... 0 0 0]\n",
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" [1 0 1 ... 0 0 0]\n",
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" [1 1 0 ... 0 0 0]\n",
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" ...\n",
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" [0 0 1 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]]\n",
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"train: [[0 1 0 ... 0 0 0]\n",
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" [1 0 0 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]\n",
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" ...\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]], test: [[0 1 0 ... 0 0 0]\n",
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" [1 0 0 ... 0 0 0]\n",
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" [0 0 1 ... 0 0 0]\n",
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" ...\n",
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" [0 0 0 ... 0 0 0]\n",
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" [1 0 0 ... 0 0 0]\n",
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" [0 0 1 ... 0 0 0]]\n",
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"train: [[0 1 0 ... 0 0 0]\n",
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" [1 0 0 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]\n",
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" ...\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 0 1 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]], test: [[1 1 1 ... 0 0 0]\n",
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" [0 1 1 ... 0 0 0]\n",
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" [0 0 1 ... 0 0 0]\n",
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" ...\n",
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" [1 1 1 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]\n",
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" [0 0 0 ... 0 0 0]]\n"
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]
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}
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],
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"source": [
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"# prepare cross validation\n",
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"kfold = KFold(11, True, 1)\n",
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"# enumerate splits\n",
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"for train, test in kfold.split(data):\n",
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" print('train: %s, test: %s' % (data[train], data[test]))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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