Наука о данных. Брендан Тирни. Читать онлайн. Newlib. NEWLIB.NET

Автор: Брендан Тирни
Издательство: Альпина Диджитал
Серия:
Жанр произведения: Базы данных
Год издания: 2018
isbn: 978-5-9614-3378-4
Скачать книгу
Н. Дж. Обучающиеся машины. – М.: Мир, 1967.

      2

      Цитата взята из приглашения на семинар «KDD – 1989». – Здесь и далее прим. авт.

      3

      Некоторые специалисты все же проводят границу между глубинным анализом данных и KDD, рассматривая первый как подраздел второго и определяя его как один из методов обнаружения знаний в базах данных.

      4

      Shmueli, Galit. 2010. “To Explain or to Predict?” Statistical Science 25 (3): 289–310. doi:10.1214/10-STS330.

      5

      Breiman, Leo. 2001. “Statistical Modeling: The Two Cultures (with Comments and a Rejoinder by the Author).” Statistical Science 16 (3): 199–231. doi:10.1214/ss/1009213726.

iVBORw0KGgoAAAANSUhEUgAAAjQAAANSCAIAAAD9M8OnAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAZdEVYdFNvZnR3YXJlAEFkb2JlIEltYWdlUmVhZHlxyWU8AAD/lUlEQVR4XuyddbyUVfe3RwWREhFBpEMQkC4FUaRDaZRGGmlppBsEJKRLUkq6u7u7+3nspLHfi3vts9/NzDmHQ6jz/M66/jifmd173Xuv71733DPH9x9FURRFCTJUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCTpUnBRFUZSgQ8VJURRFCToikTh98cUXX3311Zch8NbyXw/zJgQpRhWyTBOKoijKP0JkEScE5vz588ePHz916tTZs2cvXLhw6dIlESRRIIHXpJB++fLlixcvUvLEiRO8UH1SFEX5J4ks4vTTTz8NHz7c5/M9++yzKVOmfOmllzJlypQ9e/acOXO+8soreT3y5MnD69y5c+fIkSNz5szp06dPlixZggQJVqxY8d1335mGFEVRlL+fyCJO33777cqVK6tXr44sIVH3xbx5877//nvTkKIoivL3E1nE6fLly19//fWNGzdOnTqVIkUKIzs+X8yYMVu3bt2rV6/+/fsPHDhw0KBB/fr1a9++fbly5YixpMy0adN+/PFH05CiKIry9xOJHogQrl69WrZsWVEdeP7550m8fv36zz///JMHL65cuUKxXbt2pUuXjjIjR44kUaoriqIo/wCRTpyQmcqVK4syQcKECYmlvvzyS5PtQJg1ZcoUynTr1k3FSVEU5Z8kMopTtWrVRJnghRdeOH36dKji9PXXXx88eDBFihTdu3f/4YcfTGoY/NfBJEVujC08TNLfj+nPwyT9/Zj+Isd1N1MNmsma0eim+79IZBSn6tWrG2ny+RInTnzu3LlQxUk4f/68eRUG33777ZUrV1AvXnzzzTffffcdXQQ+QPHVV1/99NNPlBSuOpgkD7m1KA8HsuWkCn9/DIHXVKHAZQ8UlLduAWAA1Priiy+k6/ChEZmCXy+kMClyGQYNgsnzcqWLsERdCtMsSHmmb7IDEIvRmuCZxGCSrlyhAI2E0w4dUYYBcwkA+8vbQCMwI3qkcdsm2AFIp1I3nDELdCQl3Zn62YTuaIc2paM75gsxIH+lDCnyVnJ5zV/JiiAyKelFGgEGFo7XxjIUdqE6M6IpU8LBvaaUoS/K8zZwAVCdRCnMAIAXtEwLZFHXDlKypF+aJdf2YnN5i/UCh0QX5FLXM/y3crlp3GQ7SBbQC3jLyqwr97WU0eeegorIKE5Vq1Y10uTzJU2aNFRxYj+wUln9ZIW1w9kVbKGNGzd27dq1TJkyuXPnzp49e/78+evVq7do0SI2sK3I6yVLllSsWLFp06Zt27Zt165dhw4dOnp8+OGHvCWxZcuWzZs3b9CgQfny5T/44AN8x8WLF/v379+nT58xY8ZMnjx56tSpEydOHD58eKdOnebOnUvv7Ma1a9fSyLBhwyZMmECBKVOmjBs3bsCAAd26dTt27FjgvAKhEUZL+ZEjR44fP/7TTz+lF3rs3bs3bZJ78uRJxjBo0CBaJhd4MXToUALKvXv3uh6cMWPew4cPM4By5crl8qhQoQLDO3v2LLYy5Rxon66JZZk+dgAMIpbBRLxt3bo1WU2aNKldu/Zbb701bdo0jGkqezAAOt25c2evXr24Cjlz5uQqFC5cGFNjJSzg1y890kj79u0HDx7MlEePHj1ixIiPP/6YOXIdsS3j4e3nn3+OAWUBmJoOJNLpvn376LRUqVLyhYRKlSpht0uXLrk+Doe7Y8cObMWFs+al0379+mFS+QodWVxo6kou6QyMdLt+7gm9zJkzh0sm14jVQjusB/kynynkwJXiitAjI+EvMAYmvmrVKuxjCnnINT169OiQIUPeffddppktWzYWed26dSdNmsTRDRO5XXA59uzZwwJgGJ999hmmZtZMloVKyWXLlmFkOmWhkstfXtPv/PnzEZvNmzdTkilQi1za/+ijj7Zt28bsTOselGReTLBGjRp58uTJkSNH8eLFu3Tpwmp0B8MLBtOjRw9WDouBhcRykqVFYZDXrASymjVrVqdOHRKlorSg/LtERnFijxlp8vmSJUvGBgt0QHjA6dOnf/LJJ6zUUBcr2wCHiDeMGjWqaetucHDWLbKd8BSxYsV67LHHTHbYUKxs2bLsK1QzY8aMJvVucNxMhGbHjh1rku7m+eef37Vrl5+jCRUmgr8w1e4GmaSXAwcOIOEmySF69Oj4GisVDJjXffv2jRcvninhkDp1aiSQvqSwhSqVK1d+4oknTLmwocxzzz2HD2XWUlcOEGfOnKlfv36MGDFMubtBNuiXWdiLSHVclckOFybCKYFe/JYHvpLJIqJPP/20KeqQOXPm9evX20GyBtC5UCdISVl7SLhJCiFv3rykIwzSyD3BsI0bNzaVHWbMmGFH4oJBsKQp5MCZw71GrB+GwfLA8qbE3XBZ0RLmaE3E64ULF5psB+Tqjz/+YHmY93fDev7tt99CXcwImJ0CF5GRI8PypJIfceLEGThwIAOQa81fxs9JxWRHgPTp03O5I2525W8lMooTh3qzGL3dRQhCXM8GsBDss8Q5IWbKlIn1HShONMK2t8+aowT58uV76aWXXO2hOhXdhc4GZvNLboIECQiwWrRoQVhQqFAhSQQawdtaUeF4u27dOlcb8GicTOXEDRwhT5w4gUaabE/bZs+eTcXAmyGhQiO4SI7GbiNsac6hqCO5tMMLDrNPPvmkyfYeEjl16pQ9mDNNxlyzZk3JTZw4MUfg5cuX425efvllSWRgS5cuxbDSr4UWmHKJEiWkGAEoESSWQUI4OkgiYCj8pmtPlGn37t22/WeeeYYB4P7wdGiSJAKnB0791ufS3YULF44fP+6eUWiE6IEDxKhRo6pXr47umgyfD+Wz/g6QJf6WLl1acl988UUaX7FiBdFhypQpJZGLSxAgsk1FrER3RGaSC6wTfC7DoAAzwvhp0qSRLNwuQcPp06dtjxGBwlTZvn37q6++Ku0IRYoUYRh+TdEjs8DOppAHkRAtMCRbmAvKdS9atKgUiBYtGmcmIkUuPYGpK7dcL66FXBqqszg5GJFoFwwaL1pL1sGDB9kskg5Zs2ZlpdEvuai+PSfRPpYnbHKHxEXkzCddc40IdxYvXsxqt4sHsLNcaxoEtjCJnF3Yblypnj172nMMw2MubEl0HWEjhavJxaKWdKf8u0RGcbKeBdCV0aNHT5w48VMP3BN/WcRvvfUWucWLFw90pqjXggULnnrqKWmhVq1aJ0+eZHOyhUaOHGnjhmLFirnCxgvesvTlW1aDBw/mqIgK/v777/PmzZMq8Prrr7uukC198+bNBg0amGzPR9++fdvdP7TDjjXZPh+Og2bFh0YQWqMXK5zApEixx2EcHH7qhRdekFz8Ai7GHSfuoG3btpKLSfEpN27cwCbXrl07duwYe16y8BT4GtusQCMUQ/wowAmd8nQtlnnnnXekIhDFcoawPeLIUGWOupKbPHnyDRs20A5l+MuBo3379pIFuDM8PhdO6jLf69evS49ClSpVfv31VwqwPG7duuWagiEdPnxY7izRO2Xq1KkjWZxsyKIpZspfPHLChAkl64033hB3LLUowAkgUaJEkhs3blzkStSLBletWiXxN+cD7MwU/EwUEajCwmjVqpV0IaCCRLd+a5geCT5MiRA4RjAFu654wRKyTp+DBQbkoghcbnaNe1jp3r27K//Yn6uAbKDT5LImZYPQLObt0aOH1AIGTArp5NqDI+eMyZMn05GrrAx71qxZVhSJ2FgnTI1aNG6/H4L2ELlSEYNgzFSpUpHImYNeWJNcBRkSxI4dGz2mEbJkMZAV1rO7yj9PZBen8ME5siVMTQ92LFJkve2bb77J9mArksWaZjtt3boVYeO8yTHWBkACO5CTo/xExZo1a9hRJFIdqZPWAHHyO+oy4C5duphsn4/XOC+T5+1YlIBIxWT7fJUrV3YLRBB6wd2YJny+9957z21EZm19K8fMQ4cO2U8CGPDatWutWnO4xguQziwAl+e2jH5bL2bBCPgdcjnOY0yZPgNwn6tEnBiklAcmbgM14FRBRybPGzDXwv3OQNq0aYnPrN+hR9dBU9JeaOaFz7I3smLGjGkvJbXmzp0r6cC8cMGky0xxc8zd5Pl8zIjyXpN31gaqLI4SRPAYpJA3b14SmQ5z91sz9wWGlXOMey/Lbw0zTkZVsmRJsmzQCW5wCZjaDfU6duzI7KgruTJZwg6T7anXli1bZCNYuIIbN25MkiQJBdB7sRUtf/TRR1ILOnfuzJUinb+cEUnBOMTckmjBgOhKhgwZpBZbjHZkPDIjTgY2JMKSTJnreOTIERQoV65c7DV2H42wjO0Z69lnn6UA9qcFWpNwkFj8YS6B8giJjOJUqlQpWZ3Agmbt4hPvfHafK1eOHDk43dvzb/Xq1d2NDVRnO0kuTJ8+nRST58H+ZKuwGVjifjfW2AYc9gks2DBEHuLcKbl06VLTnHfi9hMndrjr8vAIVjboiHZwuybPA13xG1JEwDGNHz/eNOHz1a5d2xUndjWe3Uogu1p8q+RiIvsAJEEVG/6XX36hOm0yOwZJig0oMT4pUtFCyTFjxpDrigQtMAypBa44YWTiJCuH6dKlIxHvI7kCXW/atMn9RHDYsGHW/5LrfjTiJ06ENfaeLQcRDuBYgIvCpbHBBKds4mB8tJ0pLXBQiBYtmhRg8HbAjI3C9kzDGjh69CgrBBfcokULUgjdaNya9MHAsOXLl6c1pNquYVa4KxuMk4MR6Zyr3OCyf//+drQMw/0hFYRn7969fi6bMmfPnnVvODdq1Chw4ZGCPsWPH59GCGh4i63cQF/WM6bbuXMni4RiKJO79gRqjRgxwtTx+SZNmvTXX39RjAaZNbNjXvnz55dcRsXxgkSuY7FixQYMGCAXl4vImEUsgStImC42pwArsECBAvv377enLuXfJdKJE6tQjo1CxowZ2XWsbJYysC7xQTjigQMHkvvuu+/KshbExbgfcoS6lGkBzBsHOtqzZ8/jjz+OnPBWnCldr1ixQhoExIkUtzo70P0Y2YoTxXBwcks9c+bMkgt16tQJ9BH3hM0/YcIE04TXiJxzBXY1B3/7CRBnW7qWXc1fTqN2wxNnVK1atUaNGvytWLEips6XLx/ztXdj0qRJI08BSMsCM+revTu5nNCtY+KFez9z+PDhdl68aNeunckILcAFbIjB3c9gRBfFtlhv8eLFJsMTErmtByir6wcJIKRxLjQ+Wj6cgLhx43JCR5XRlQoVKiBaBECpU6fm+koBTjxMU66ynzgRg7LGbKSFomDGh/SJzAt5EwdNXOve32vYsKG1Khda4tHPP/8cQZIC0LVrV1uG+X766acmw/tYiMZlIi6U5yRkCnlHBOYYWIyLhRyyWVgkKNCff/7JOcPU8fl69+6NzHDGeumllzhtLFq0yA7DQu9cL7nTLrCuuGSsMXYolxWhZQuw9iQ3SpQo9CiHPCpaw3I5OGeEKk5AMQoHjl/5t4js4oTSkMiqZR0LrE6cGl4Ml1qoUCH3mI96cRi3t9rDetIvLFj67D0qul6SLbR69Wrr0Ti7uT0Ce9VPnNjtNMWpUO7e4KZnz54tufBg4oRZ3M9gXnnlFbqgawZD1vXr1y9evGgjJ3dXM/6VK1fa8YcF8QSuB6+BX3Y9goDHrFWrFsXcR7Po3X0CbfTo0TIv7MbY3KdImjVrFujRgPJu7OV6T1pYtmyZyfA+FJk/f/6MGTPmzJmDtREeEpkvh27xcVTBFDNnzpTyYfHYY4/JTDEI3hajSXf8pREb47JyONa0bt1a3nbp0kVuhD4MtI9VWc8MgFCV9gnQpX0OExKps4C3bt3K2DjTELS566pt27bWhrxo0qSJyfD5/HaBBfPKGU6gO85efgEWiDbIHTkmTmDkPijIGAitxDLEakQ2gS2wxUi3N0XDgigZyzN9rgLSK2PGLGDbcc9YL7zwwokTJ+xSdEsqwYCK08uhPjzKOv7ss8/mzp3rulGWO/7L1Lx/cbKb2XUE+D5OeTawQJysdxYo6ToRTri//fYb3idLliy8bdOmDUd+d1QPJk54hG3bttmzJ5QrVw65WrhwIR65Z8+e2bJlMxnebz4hjWIZRouim