555 lines
242 KiB
HTML
555 lines
242 KiB
HTML
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<head>
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<meta name="viewport" content="width=device-width, initial-scale=1" />
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<meta name="author" content="Jin Zhang" />
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<meta name="date" content="2021-07-15" />
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<title>Task05_Model.knit</title>
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// v0.0.1
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// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
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1nJtTNAj6jJKAAZEs8WgqihJRgX9wJHOkYoXkf8iwR2RiKKqRRiitWw3lYdnr30cDzNae/8Tw/1L3sS5gFALINXpKDQgmp1pQxW86M3O8aoqMTlNtTGnSjATM2tjXEgCYfS3hKyuCkFHkzBeScI6WKhFVxLuD+EQLt4TkOo6CU5f1drrhvrrVly/dspDayfe+8EtQx7fuJG0HcbZLyyc1r+5qXbojtE1xa0dt4x/5c31r9hA6MYtP5DrVgijoiV5Po6KKs3MBOCVStFlgez8bG57v8/vq4tZ/Gilfr8pX7VqJm1EzJQGeg3j5/xX8ruWMbrG4oduFyXxMEFyQlkpkMeJTvhKbCMY1j/o2ykPlEmSr335KxvYPvbZydev29P65KNrX58+c92zfxv6+Kil76PnU1Sl6fe+l694//zIweMjUO1ZPnH2TU3fxqa09+l/6OHXAQgEAaSZuhddMDiaZ1epkRAzpTKAxyVzrnGh7JLreGi7qF1VqO5WvoGQ0DwF584uo3cpz4sCBzc9T9SAQPKgoqI082X2QfxhshCzXmZ5Jmoo6MvOYAk7gCWH6cudN5+98oSroZZNBoRWbuEw1ygDmqI9OZ36aJrbbTPYqIFmZrldRpdFA27ONADF4/HXxjyKYhkRU9LgYsIJ6e+pgHAkGUjkgUhLSBg2N9w3IMwpylMaKScT/n6efcC+PLN8xActmMGOhu+4bH6EpsV/yAgOoO0n9/+HnR2B5h7hr455LAPJ1+wc+1i1AYGhXOs6eQf4IR+uigYUp8WSlweZTnAWFNpz6mJ2u4d60kbEPGnUwENEvUTbVJbqTCjIAQJlPo8IXEUNdQEJcCAhMvd/gvy8Q3E6TmsbErv++Z2tRuuN/7f1X+zsNyv/vYhoN066sbVlcRuZiq/iWvuP7rEb/7LuhyPfsFPLMffdxfMnz7+1fu5qEc0RPdM6QIHLo14FgCDKRFYNMiWU1MaoAsLfupYpQwobhpDby4OfkoJ4iZQWPyy9jNLm8wLSdEtUyzvBB3lwOVwbLXYqnl6U+o3+Qo/Hnp1ttBtL+ihOZyBQXGwBS0Z9zJIGwfoYXGwTYYlLnVeWdKFwoCSqAj0/LqoW8qk7kShFiku3kK9cfCPVHyDedt/qpeyLL06zk4uXtU1DyfXfE2fPmrng0Ccjbhg+flxtq7zz3ZUzXhrU/O6sjqN73mrbXD2iY/Kzm89vbBp7Y/3VcwaOI3vqq674XdnlYysH1Ym8GajvcgekQQFURnOzZJfFEgyCCwqLtNy6mKZRrzd9RMyrUkMdR+Nfdbfu7DIBzCIaw0J5kS16edcXuNOdBXwbyU1J1ewxtvTOqxtHP/3+JIOl3xOz3v0nmr9Y+f2d8VNjp4xrbbm7jQ5mdazJdtYzasufW2r+83/H0fEE+3DTXbdNum1+Hfd4stOSZuvMURh1OXnyAPjtnsaYXeumMPAnaOwXTOb4NVYT72PqU+xG7xcf6mPNQAQX6/IUcHKmcllV1UUlBRXFZdIaYyZNUjgzJ6Rpm8u6mKrApzM0vUgYbrTrbF2SFHbS18Xa5GhSmF5P7JYqZODSiqKajIK/VYNEqQIEZRigFxShVFwJURhGD6JU0ZlDP443kvW7ccNSPH2abWFfCns140peoYDeNeZHHSqlRgkMcp00ViJSV30QKhkjagSue7JMQH4304/FkrTgKC9Tjh69VLueUScBrhFPNVAUJJTKEur6Ce0u1dCFuorNZH28UayJb2IaDjjNtKWsWmioXPicrpB365FYFc3LTU9PA+B2dlqdhUV2QCMFCAazGmNBl900ImaXkg7mVCR4KJVkyfpRJFR5F86oRckaXOFoe0m/7W6YevPVY5uWvzf1w3P7vm99YGyIHU4139VjH6ob1tLvqqpxR9u2r5m2onVI9RVXsHUX9eMTLkxQdnCc6AuVEIv2VCsq3G5XOGzt77rMZaWBtEDvNOgN0au8hkhEMg3QTPzqkVUq5feAklS7rOucMleiPU7ivc6kQtuiYCqrfNTdlVF8fxLxCKgtj3iUQC44+jrzOa06UfyDSESH3x2j106vnpWmTXnhlT1o+UfT/qt9NdGau79/Zhf73+exCP2T2Pz/ZefZXez6I/gIyv/EkRs7Yf3IFpM1FG27n5x++NQ9Q/otPPTGQSQBH/Pd/9Yf/vjjne1sx152gh0p6f3eKHwYW3/EZZ93sA627uCCpcfMzwj7AIC8WN4IKljh6miAWKkBQZHNZgqip6CSZLOSmpjVSs0yBZocIpTouZRiZWGortKL8gsDiITjI5Uik+LHJ7FXiYTziRJnywoMgWdwNFstbzxXRcbikdvy72CqiPvXAaQznI/t4Idczsm9VLdbktKzzeY83vfZ7QGDlqalDY9ZNLRSTbODPb0mZneCvyYG9BLcSxY9KQVDSTe5ArmSp7voCQYwWfE4HPqnwOu4AyOYNn/C/fPZh2fjx7C84/aZ8xev2nXHraxT3vDKpkVrHaacdQ++/xGdXTuy8Zr4NrZo3PgNgDCXI/UBnh9eKI36VZeLN+NWnxscUBNzSKpskmtiJleyNBOvSfVEKuQRD2+0Iw4l2BUdoTI+ZiikBS+9h9OfOtrxL7aJvdiOkQOHDrc2tEs72U/HmW846xyGi3DSZ3j9azd1FvUDImwoz+E2NIBd1OtGAIdVkjTZUhOTqWTlLbMzaamUcEELnGVzAbVA0BHKleew8ew2Ng534wR8gL3Dxq5ZjO/xGuQP7A55A7ubrcHDnUMBdY8RLs0Mg6L5BgnAqphMiBbFWBOzKNxLAnII3zehaKqJofOXXkp5iCsitPAkbol0bqDV8RN4ijmIm4tl7zK2BLqkUsalGqFvNN1AqVkBQDQJoSl5QlZS0MVSLhaCX7P9dHD8OHKMEwKWxLu8KBdxL6ZDTbQo3e8nNquVEFemy2DIsGlmjQdbOr9BNkt+r+zlsmTu1FB3wd0z5VlnstgW8BBwKLpv9YJL5RlPdMKNOALkU1L14E93sr+yVfg43vTxgZtW/GXnd1vevKGVHafhuOnyAlyMU3AcPjDybB377rOT591Y2mUHeYJu/Ug004jIzW+QJFm2GGhNrMaABoNsUijK3QmbMnfKFN2XPIHtjr/NdmE5uRrDZG78Xj5t2EIGAOCFiawBT+ozgRw+bSAGXiPLwM0MRsr79e4NCw4Rxa5IJL6kRnJurq0bOKEZy79hDV4k7gVL5JHn1l4AdgYS+tfxVS0wMJpjIcRkNiOAzUBl2cq/UrNZoXwP3VtwpgBXF1eWAOXEQAdVfSMRDKBcx1awhYvEZm7FB7CZETKxJf4D39CN6/Hf8XkJ6VIlly6LPUkqBVCQArccJKJUl6GXoPq6r3PD1MsbzldfSPxvRcyR3dAvmukGo9nI1bbxUPHKisdJjEQxq9QGilBcN36X0mUp6hA6Y9DpEYujXuXykscVRBpkK4wudhzbcaSC07GdfUgtRrZEms9Wzok3cw1WSi3nqklH6R3oPr8kYcedOm6WR9NMYETFagVwUFlRVM1MVW5RVLtHv11adI/EnAKwL1KEcM/JO9nv43fpSiwh81U7+qQGdrQtXseFv4FZvycdQPQ8+VKfDHgE0jgAfBZF8RpdNTGjRO01Mer6daQROSBexQQy16Hxpkj+kj3BXubXE3gz1vNr/PlDb76Bs9nSNzaSY+xxdivejVP5tZCj0mP/OYvf4smfoAvtpHU62rkEFkhGowdsNrvdbQXBV3ZNM9TENGr/TSzoRn/ZLXHoEyAo4ckJSx+au+BBspEdYacX8yA6iCb0UGXmlKkTd504Fz8rb/gchAXYat0CdkjjEZynUFmSCDVIJg9AhmYypVOVEwBXRFK5UWSV22N7Ev4uHU92T9OQe+LX7PPaKziWzWZnfL9pJMZW1bO5OPS3LSUP1S3lg9poocvnk0ySppm8njQw8cTzu4wWMA6PAZgtFm40C/WaRcikzJbSWfPzuXKqQ0sxKLdfgl3BF0A82brsgaXLW7gB12EPzH7oTqxuZWvZKtp73M0Tm+Pz4vvlDUeOLdxZwVwPk1KRVS2cQX0ce4s4n+RlpKcHICC7LeCGy4rdAbAELNlGX3ZNzCdRYyq+uhvwVHHWrRpn+IvGGoVFl/MhDadWMcJP9LZen9cr+din7JuOx/ZeN2FqnzFL7767DtWvZu2f2TrnyermlsJrn977BC7f/lkz5g4srx3e8+orqypveeqmzf8qL/13n8KGgcUDKqrHbRP6FwNIYiqrimdLCgBFNBhVKlHOuxSdv3y2lARgcoLtYrOlOn53IGEMEF7k+dXC13JCQdThQHSbDQaX08hRhsdSYuuXVBAOtyLx4BHI6+6CYLnlEXbyLfYFex/D9zz7BAf0ztqVZ+7EwHn6YufCPz33/DraBqjXfyHBI2K+RonRKAOiVZYkC3BDJ+q9VNpUJOaj+sXtVx6h57CC2dmLTMMKdPlKFXO0a4DY+dTwvZeN/qJLhrqRy8gSsx+T0e52yQh+v2ynlszMrKwci9mcnemSzdRvt6NJiOSi+EtCbgo1UyM3WkiKOMKJUtMlGvCIi78nPihD2fPbzWFJ6WPdxqngfix9q9Sr9HQdwoJDth5mUy/nm1hKoRixV/mpUJxwVT85trLi1EAa6twb+aS+9uuhNBsStmnSbVMVzTXLnPpUo6oYTYpJ0C2VLGYDkWXJqFCUkhDL9evG+ooUZ3VpjZj8Izex59h6fnXg56wfNmF/DGMtC5Pi+GHyHdka/47Y4j27dJCYyF2B7wZVlZEQEERvNFFF4QqiSgVDdslOjEH5Z65AarLLowIDZAGWchEZbA/LwDo6mozsXBTfQUqoXleVJiZ0RugfzTJISFUVEExmlYuSRP1I0IAGUcZdOgxNpl1qFqqPbALSzPPvkbfjTVJ6vIrs30m/RXi/0ykkLWUbyWw9T7KjVgXRIIFRJlTBfN2EuvH0BNZX4iUpmc0y8bOPPmIblXMHz60Xa1gA6MDkVFt/ZIKYnGpfnBa6sUmAHY9/mJhqI4S4fJ+QL55xoKIY+VYNoOZTiaaCvQtCfCFHMMy1CH34IX7GMmfKjQd/UoR8AzFIA+R3QIHeUTdBWVYkSTznFd6SVJko0DW+xLKLeyTRZYcwiGjADQ/jqVO8uP6KGOiGzmqyKN4maq1OtpHWXhja9SRIRonoRhEaJZ5K0NrOFyl//vMAAGKNdIQ+qATAwK1gBjVKRVTIdwCUpB/rioP0XWLww7EvHPD6PGRL5ZkqbKpcLx3ptW2gZ/z7GYIdmjju9pfm6E8Zq6OFTovBQvLy/P78LIMhaEkbFrNYZLfbPjjm5jWdnDM4JnvBk0Az/y+ZVYSeXlcUJWdMvMcN9+1u8h0omny9N6YT+huGr1r0xzd+Or/5xbv/On7T8Y9PswO/X3znY5MWPHHDsNfXvfono1K6rn7f+K3vx32E27h55MJbxwOBFVznDsUNTsjh7BvIojRg1Mw2n89szrWA2WPUFFDSh8QUL7iGxEC7mCz83SHi7H5mUeZ0aISzRVANCgTlw1AfH9d2D8WobftHX+7YNsMT+hpLLZbJM2ZOJJNvaZk+Q5rNdrPv2XH2t6XzFTdbPuiJ9jP3rwh0PPOXNWvWAMLoCyfoMWk2eDi6esRYymclxCubh8RkDexcM++lZZJuOTk32SdwmnJoYkjgUBQyIf4DZqJx81Mjh9525cmTzcuHVf/BTQZgFvauOZFVwBH49ZIydr4kH4iQK81M2CcaDRi9Gi+obTZhqFy7xwIOIyi6fTTdPt5ft4+oT4Q+ecShOXlPGioU/BLkji3iOnVPiAnZ9vHnOw9ON/mw7Jv+1omT5kyVp7dNmDnLjWVoRx7zq9vG4YSfTjyy5vt7ViWNk9BynD61y+DMEKROSUpzOLKcJlOm3+OkzuoYFVUUVMesmuoZHFNTel5aloiry3bI3RbgrbNeR4XKwOMJ6AVAxMMtOP2GaQZcT2aVs+/Y3zDt7LdoiJfID985vmNc3Qb61PyZM+d3NmAPdGAahth3Jx+789Eel5+4rCjB7nSOkgMeuCKa7SZElSn1+qwAPhndyHVz283akJgZqJ4bgp8v7QVDiRwWFgxH9KfOeieocBWpiZ1l+9eu3bj/ufm1o2uv6ocGOq9zCZ23rKHh3ZdLPsoafsVgoKAwtzSV26sYyiEKd0SrzFlZAwZIfRwOUqzmSkGUpIHpPXr4fJFg8Kp0K1jRqlj7qv2GxYy5Eke5wr7FpDpWXFxYWDksVqi5e1fH3BkXz+n4pxIOWz79gRHv0LneqJs2FQ76ewKfPao+pSsqEvmsj+ykQFfCF6ZeRcGFyUQK8v26El/4WGzqS33OfxjpXbL2ndc3sTfYvm9+vP3WksHVg5tvOnmsZKGTFc2buvrNabOfa5w5/drrmura10otT/ceNqZjJ5Xzew187smt/1i1bPw9We5Roeh1xYVrZ732vkM6L1UOHVlb2WcEHT5q0qRRuwBhBYC0lmeDB8LRdATw2Y0Wg8Fo9Nolp1MaEnNqJkCjR6D/JfU5336yUOPaKqJJEuCQeFQirWX7O+6YxfZjqapqE/61bQ958LsXt8S/40CwpeDekav/vh0ILAPAD7lsA1jEZFcyGsFksprtJg9Rr4kR6DJ/ZWoO7uobKtNnnyJUlrW3X3ttO14phMgLHn98yIjzPqkFgFxoY259XSt4oSTqd/L0JgaDT/NcE9PAaBctOk/sjOTEKYEwCRGJxwB6tajQpMDBcxoHXzN8CJbum6GLZe60066mRmnd+eJXN6mThXRIWPMH/Un+NdGgxLmTUKrIsmYzWa0Gg8lkN4P41WCzUcXkofbu2oTf3cjSZdpuokXRuGOyi1dx22KswGZWhYd5AffOIrF9jYxdh40sI74Et93MVivueDXr0gYPcG0ouF4DRIkAevQioLvExgPivyvuhO7qQJ5BQRgeLXS7XPrsKDMzI6PAajSaTPkuq9WRKzu46XwOzWzPRJNH7+G7krl7+OC8ePqbjJDCRIiEfKFykdziVfBd8q+ke9n++uvnTGL7vy529F437Xwso/dL097ZwvbVXz9jOnlw3rz12+LfSS1Lh1+/urZpy+F4kfhtxYuQjGCut1tMFxHAq6vrscoOoatQFU0Xx29SyV/XLRG8TS0ierkyof+ZtWWXEPbn7boC9dce3JHE5yf0pzhpostXLJYMcLnSvcYhMa9mp0Nidu8vu/xUrvPeVQMOCCQs6MzrxGVT5986ecr8W6dQmX3ELvzxh7swGyl/I6Xt6/70Qnv7mhfYKbbnQTS8jE7s8wA7B4LrOep1cC1ckMMn1Hl+RVFNlKpZmqrlcuQEq9U9hBOEwa5mQEaKzBKmSBWoSQVlTvPepDFCnPndRKFJtuemosq2GZrG9p/taZv8wfaPbt58TGf7vePdSx/wsv5K9SPtbB87/T/s7H10mU722JDgM67pTN1euaIq8dIsyh+TpOUZ+fg6PcNnz/ZanE5V4I0FhsQsv8m6iSfIBUmS5S2dL8HBXl8ook+LIkFBaLdMkafPPzxZ2v7R5zsmPXeFIQMJ22e1lq48uri9oOMZ9uLa9lNYiho3Z9+6xqU/bcBDAybXN3ZFFJ3LddVEh0mcejw5BCxZZVnUS7wGFxqlMrTMRy+JIqpdWewrCD+6iu3/sre97yvSbCP7xLR8SXyH1LKxZTYkqp/1XIZ4dpmjpLktAEU5bnchWNw5lhxTli9rcMynUdPgGPX+vJ2/2BgiqPTHK2HB5clePsGgXCkPt082oetPnbx1/bDrDtW395oycuG8yJd/3/Xu6MZHa5Zcv2zRrf2wZn1HILfzsvKx+b0rCstHz73+8VXN/8y//JriK/qHR/+30LeE6xuRa8AjToRYDHa7y2UyEIfB4fWZnHbn4JjVYrfL3HVyQt3QpktOVnRhgnBcxKOXvoLpIyFPwCO6cjK3bsas9tdeeHRt8xasYDuu+TD4aeiNN0jGwgknTn4e//yqK4UOT/Gc4zM+cENZ1E8cDrfby3t/j9NoJ7JNtumyPcmJ1sVDgItr7tQYgH+grxdrpR2zt72PpSLjsXRp7XUHt5Mj8dki4Ynt/EpI9JkPcrlm6BV1m0GWiYgIK0G0GNEuC5llKWndDU1X/x0SbTfiOtaElf/INyryZYexkjVJLfFF86aMXUzaumS4AZRtXEaWOMsoSyaOIVng81ETVTMyMjNzVEXJ9plMVLbbMxQ7yDqidR3RdPz2LIDSIO1WQ8wBsin/pGskRZpuUfew19lm7LMwJ1eRcrT7sG6R5NCsqBgvN92NPdk7uARPdt4vtTDH4m9q1lxH/PGvvE03jMkcer4XnuKKI5gApOW6bWqi+YoMaKSUSAQlGWWzQVWtfIZmMSoUAA1mj4T2S2cBqaROkYZeq3KlhdkClOu/mD2BI48cxZHsMWxja46fYO2kPwmyZ7A1fiy+DRewhcJLzK17ycs1KTC73ZrXK0koahm/Jgob/pNT8no0p9XJMTHDAFyVskQJkKKvhBlTUzxHyokifvTqgNsSaw9mmBRz7n4cwoqu+vcfR9RErqqfl+fkfr2/YcZNo8ic866XXnR8Z72xNZI450HXce2MIn+oKqkIYDYgmvQhAm8c7YR/MwyOoefSIULSSMJGySlCWEwR6LrOB4nC0uhAZiCmDrLp6+3xekDI4T38Id7D54ipCHUbcnIcfn+uNTMzIFGXy8qjKd9qSbTzYosp2hbbF7bnuBrm+REWRw08Coc18VTQ4xFQ6+EJhDmL2m6/c/OZG4cpn31T3XpmM9quH32qucGAVz7Z9jEdXMUObcyzBF8xskNVg+knbU8BIO5gJWSlYgMK7tcIpZJMAaCyhONDYlbqCOKOo0cV29lA1ylOauB7yBN7yOHlOmgGQ75bkoI52TabW3Z7qCzl/3/2IIuHzuFynuSi2BZnlftyiBSnzxyCyzwcrImh4e0Xbhz2+9mfKtWtL7xTP39x26LeM2aFPyFVQ7CnuWmyw5K3EXsOrqIfh2dPY5tNjY2nGm7QTxGQIqmCtoEHIlG/Ag4zmKnd7qNeu82mSJSaHQ5QoCRU1lYi9ElBdqqp5pwa1sv/RAMmELwQB0baym968pqFwxaOC99ePv7pgf89chFZcXX5l1NzcyPRii+nphf8lzhBwpbiQanl0rP6Dg26zurbad4v56mukCugE0Wi7Vh7JsTasSV5lIO0dJbKBcljHAhLOdJqfN6cwad7QYchPV3OyCA+n4mYMrPSXCNiBtuIGMiGNH4pGWmKygXqpwH4S8+ePzvOII575nOCTh4R15lS69q26gmSEBt94OCr7YtF6z7vlm8b7mpdcN+rL/fHcyhjZk77c8arjmflv/Bn9kZObzbAuFFEB4A0ST+d2BztZXeaidFqTfd6iV/zO51ado7Fn+avjxnT0sDFqcleG3P6QR7xs+NNXUfUIJTSVqjbjT+pBpRfbpXXFSKawsFwiBuQbNyyZcyzs2sbcS679w9k3/mvbhr+6qufy7sbvojGrt10dOm6WtZ5ttes1keObtl5BAjMBCYFpHXcnkW8R87TLC6j7EsnBrDZ8jIhM/OyYp9LSycWo2xQPZ4ctYBHz/YyHc11H2qb9S+iA4oURXyC3SM+0WGqPrVIoJJaFCmMXFRdbixfuGzBqEk3j1qwfGE43Pbogt+Nn93Y9siC8v1T6+qnzxxRO50cnPC7BcsWhCMLly6MTZs8uu2RtlBo/iNtYyYOnz6ttm7aDBHpCoDEp+PghZnR/7I53U6Plce2UaYyMYkJqxeRED/HBp/idDkbYkCRuuwmm93WEFPtdgt6FMsl5xX9mtiW3kNfypcpEhAfkgPKkCfoEXdAGF7cGCBD0YAVbOGWH374gX38448/vsOW4BViZBv3vHrfq8eO8RdyHMhFiKNCMGoniiKGmUaJSlTVsUcEbCpFdAhyJGBIAFHnAbag8wAAgUm89lnw/0o5D7g2jvTvPzOzu9KCJNSFaAKEBMYHAokSuQpiY04OODjYsWxCcjbkNaluuPdyiXuaS0jHpPfeE0N68fVO/ObSe+8uy39mVlqEzr76oeyi+bG7U3bK83yfkUZBGZwCMyKlaRaXRRTLC6E4JyfkAld4DKmpsbkrK0ttpSafxzc15nHqTVNjepQycUvmivi5NiuyMYtA0qyNo3NOVr9OFfZJmt75WUW7VMhOWtE4fsubj9zRP33SzuaW6LxFB3rWTJj4xSuvXdHyYsOAb/bpj257c+OS5s4tvmrim7appHXPputbn8kPlVdURssit194/xklXdGr7p3261Hh7uKKUGH0uu2nzi8Pxya1V5qmAUYu4UfygiRwVi0/YrQaWIvIdGcQ4pBB7dzU9snCdpLZJF/SOXJNjdRPPa0uMhVd2TKurqk5Mq5FXFPXEB0/7ucNExvqGieOb6wDIIw7lSbR99oBPqhmvm9ikm0mm7/c7yzPc+bV1IrpYEmnX1mlhbZglpActKMVbEo36zBrHWyifBGnSASrw44ZvIhr6bwgFCxiuH4R45HIul+c91p4c3j55tf/fvilPddGFx5b8zJqf5X9DCi9v/m10vvcrj6U09uHsg/0Ke/29invHSBfX7VJ+TAv99nwkcNvfNd82xjlI/4/Su+rLyi3/ObXaPaLTJb0b6xlBfCX+DHKMLqgAOoieZk65HLlmXXU56PLK/RmGI2e9HQbys4GEGweShSEA0F1mAtak3BQbR1SPGxVVo3K6irbp3YM1ToJV3pGr452r7n58XnrWi6tr79h3tY9yqTy/KbYvMvxsYvGRLrPu/BCWegef0l+cNcmpeGP/qIz6oqkNPas06Fd6BEEkMAIbZHRaUaDTKd2RMKCgERqGDdkGNkrBpBGCE4XBIMoIpOMsR4lWko4kLBqJI+K5j8Faab66Q897w8yR4ALIR3yqYfpaPGg8hFyDSo70RG06A12/oayC49HL1E/s9K3DL2QNXzKGb8fhTCZCCJkRZgzSkcQkogAAdYJoQTf6LXQWZQQHjx2hLz1I7pgEIaGErEHWAIzAAhaezTEW+S5kUqBYFHUgcViJEbamxB9uT/ROLFE8QLBIegdsp5+naSN8spKbara53ErgY4FlFnoIwadmhP5X7VaYcvuz5QHAu8h/cO3K+s89eFTJuceP+dft9utd0xUFqDpyj3kqh3K1+H6uhrlzX/ZctHQEckuSNLhJG8MjPTGCNLRbwWDZH+Fr/6Jm7D5hAmyIDMiQ0ZGTrbVkMkqRQ3FUq17vL06HSowmDyctbXd2N5201ln3XjW5a88G6uvnz2nLjJHWMg+7W0766bZL10emd02YWJ7G+NFAYSwiCGdcx+ZGTqdRB35BoSomd9sMRrSZYQkAYOKeoYC8S5MM5WnxriwyfZwnAs9I2/h3kG0RVlFY12UNylYiiCAo/gZTriVRKwOA5LAgiyuTNnkwQ4Hyucer4lJXb96j39EPHUF+JnjK/5+briipGXeqiuf3np9+4YudA6O3jbYEQv6S2bt37Cle8be7rMBwVgcxo+Ir4APJkRy7enY7QbIl/LTzVK65C8mdrvDIed4PSa5IIE5pbQ8dlABTRX6S6xu1DgHrezj3QjuuaN9/n1P7N541ards5oXtJ3REgwFWsOdE/b9v3W9wlu7a432i6at2N7wzOzzq6tvrAr76ePuDExYn+qLI0JEDyCnCdwXdyjui3uFjR/VNMjMIUk6ao6YiGZWHZ0i/DX75U5H1aEgAOK2LmrkhkxmMUmXJFnOsjrBQR/drXNlOGl7yiCq4Y2Z+zTTkbYwT8qwtv73xo0CxS6XhZtDZ7WvpVaAD0ZnlC6fNWF+vigy+yj67YoVdz/PrAF7Z8wo/9mM65SDUhQQLFSOCbslO2RAIOJINwsiAoTMFr0emUykKWYSWc8XiHtk4gMlbe5qgAb7UsMIa0IFwu6bbumd0PqX1/72IW5Tjkmn/3QfCVmPHEWCwiKd8Cj0e7KGEUURmUU6Ebk1RiCQCHSypSLhfEr/+2Eqe2hQsaNeALBCVcRlNjI7Fh1Y7Gaz0W60ySYW9pXNXt9QQI0EXB1/3PjAIiZPQYprQ3RWgnr3Xd88KXuOu/GW5v7s6Kwj6xc5btOZJpzh7hmf2cktXDiKGxPRSYI8MjopD+WfMDoJeePRSb4QbvyciNkVzReismdxFD2z4Oyi0vHr6MwOwnTUfEt8ic9KPBFjIvYqgzhkDw/xTGK3kxc9YlKPgt969IarH3/wwP4nFG9dY+PEiY2NdULbnf0v3Hr7wAu3dHR2dnTMm5cy6s2OlKZTy49OL2AW1Ib01FNiGh70BD7YIdHEB79/Oej1B9UBL+6NL0aoFonqQehRdg4ip/LxIFqsSMPn2KuMXYbaUNsyJZw1fMrGrnIA6Qpa2n5Y+TuAYvg1fgUA6eAP5Nrjj4L8IMFW+uJUVye0D51Au5h8T7W6B7CZSZlyNlXeJ75ClUs8XEnM8as+Eb9qmXpVwDBeWUH+LLTzNU5DpKiQug4YJk0jh0pMoyDbnI1lQp0JPk9rzJdhoRy8xZvKwaN4g9Cm5HHsnddbrUub3bCVWHLF4ldiF1wYPjM27aFzzp37w3lvHP3F7rOrUcnw6jY6d1dT86yJ4eiY0sOnTO6//YLru+j0cyyamXhHhoZU2lu3GPuhiOexHiQ0HfQPYqfoh9HVJ1B0w2//heIgzFQV2SMV52iKgYTCOlIxU1N0cUXaQwR7uWRYkxbXSNDfPYvXhpfEa4MpdD7OPtrg4sg4yUbMNmIRLCjNZEJsvgbgEETRbiYUvqb4syENGQkj/JFkkzkxTAQrMmlscsKiQLvUAAeUNb8G7yQ062PCs0QKkEYsI9rR6nzH9imOvcoLeLew9/ghbKIUT+hoLlq5jiPvcYqZDnXNrC6WKXZGjNP8+VlGYAXOBfY556p5+ZaodTT0KC89ZE+UXqqiG9pSFPdShT1JcXDoO1XhHnmNmZqia+gnXgMYFag1wGbucZ7cAJnQGCmivUCW3ep0GlBamtthAIqVWwGovcRJi9eKLYy8TgmP0+BgddahWmkscQqUlpiPo4MhBwPPA1tV5FzFz7cKwm9+d+CzzzahATIdd1Du/G5GoOPWnR9+ofQoyl1qHsRXeDuriLez36eUA+dUeTlUxtt7N1fgvJMpulHDv1AchOdUhXek4hxNMZBQZI1UzNQUXVzB2vvoeGkj2IAMglnogXTIjaRLBGTZYORGZXcgqMUn8260FqnLBlSM7lL+uB+Vocqr6Rhetkf5tfL7vfj3qKxH+SMavZf++VuaSiUAhD7DLeIHkgA2yIZCCEdyXJ4cuz0tB9LAW+TMK3Ab3QxXJQWpdOWImbyK8arGGFaJqpEG2V2IO/yqihEFV1Wm94Xts3tnv8iA1RevaL1x1sDRP56CjrR2UWL1/ZBiOG0+WqzyvXWXXHDpANrEwNWGNfM3DSi/fHYJ/rbsp+8e6j5uKR4aUmlIXgO18Vocrdaz1uOkKrqR6V8oDkKPqsgfqZipKbq4gr0RJcl9kqDwq4yNv3kb1KtYuCSJSmbrqZpIDiOjjbIoSpJTMDbFZEdTTJAFWdIRyZowKGrdjOZBjePIDroW0tZGwh2UUz1yNcPaH1CQ4fikjst3rbt0NcHv/agMUij5c2Vc18rz5/NZJM3JfMkD1dAaGU3tegXFxQDlWSZTbXkgUGPKKtBBcbEui2SWhkqnxEIQcFgyozFLwnGq7ZUx0g03TH/aTYLqcnOkuuX8iaFL8zhXsVAn4a3SSDRSWl1/RVfoo3fmXTau+ubIbfnTo2vnNjQ0TVjXsWQjbb4+hL9FfuGvkV+cNqai1JldVTJn7srmu+7JLfy6KLhqVGhcaeOylsh5lbWnl49r6TrnKPVMv/LO/azH5ASbVEBr5VQ+UtQfAPb2jbbEazY1vfvCE6Xna+kHfxhi6RUj001a+kAasPTikemClt4lAX+3T+GCYcUDmqJ/lKrwqwogTCEpQjeUQBBOgS2RydU1JDM/P2g3GoNBuabG7/GMKZPlsC/fW50fjVVXsyDp7OxQNJZtNo6aSoF3p+S0NFDHPHgbYiBJgQZGv/ERLZmZ0t5q6wkJKnqMhzBz8MufZG0ZXsZRzHYYrWJk1TDShwoZfiVWbn2rce4L19/03NdfPRtr2nHzvKc/emdx/d3LDyM4XkaJq+cfm/bY8bqFq1fv6FyOvX+1oHvwefbOru7Y0zcz5q91cn3Tq52bInXKZx9RCGvWp8UlOEsQzpxD6T/05acLVrNap952xtZhP0xWx0+0iY+fnCrjtT1FbQ2389oqStRWanr34n+eflDP00eNTBe09C6rWpeVidoeugYAvcGv8LTaXynTgF0DGRLXuBwA/y5J0T00eaRi6JdU8UmS4qDyuqqwJBTvUMXlkqApuriC9Vdu9UkSBIfk5fPVpZGx4MYuV46oJ+kEY0tOTnr6qEKLpcQNmZh+SJ2ImdjppB56CnnSKS02+RpiJifBU2MEnYC8izsQ2clwI9I+1YYLf3Gtkw8SVgdtm4XAwyNdtX46hDAvXCL2GCmnN3ZetuitjjuuvUr5/0PfKX9DwuFDDfpT17zfga0rz19x8fIFq84TXdXF99Wdtr1n/m5lz4fKh8pLyPrJR8gyV+hdtuva4/Mv2Lj1ih27+lg74MwMf2tPV9/aEPAZUHI97ucl3KK2k5t4PReeOJ319ZfAyRW8pRiS+gUt3aSlD6jpeSPTBS29y6C2pIDWK8yCw0JYeIl7wbKhNGJ1pqWZBQEIyYUcNwVKAXHz0vPBYdBQiw8WTxJRTWOGj2+K1tf/PFpXNzVaf2ojO+KOwcEvTpva/POG6c1EmNrUMqWhpRkIfcaHKAN0OZ81eEfOGnzxWQOjb0jBFAZx/C+zhmCNsJ9hQWsvOLVn0n5GBm1eUrt/zK5jR21o/OiJKy9AhwzKa/6alefjSoYJlXV2dVyL7IwUqpp+Qes1ytH2RjTouvnWlnFKMOP2oSGVpeD1c2ZST4ByefGmpvMavgVOruA1XMnTC0emC1p6V0B9A0u1np97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<body>
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<section class="page-header">
|
||
<h1 class="title toc-ignore project-name"><div class="line-block">DataWhale 组队学习 R语言数据分析<br />
|
||
Task05 模型建立</div></h1>
|
||
<h4 class="author project-author">Jin Zhang</h4>
|
||
<h4 class="date project-date">2021-07-15</h4>
|
||
</section>
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" style="width:100.0%" /></p>
|
||
<p>Task05共计3个知识点，预计需学习2-3小时，请安排好学习任务。</p>
|
||
<div id="前言" class="section level2">
|
||
<h2>1 前言</h2>
|
||
<p>为了帮助大家更好的使用R语言进行建模分析，本章节将借助波士顿房价数据集来展示常见的模型。本章节学习的目的是帮助大家了解模型的适用范围以及如何建模，不会对模型的底层原理进行深入的研究。并且迫于时间和精力有限，本章节仅介绍部分模型的实现。</p>
|
||
<ul>
|
||
<li><p>回归模型： 回归模型是一种有监督的、预测性的建模技术，它研究的是因变量和自变量之间的关系。</p></li>
|
||
<li><p>分类模型： 分类模型也是一种有监督的机器学习模型。与回归模型不同的是，其标签(因变量)通常是有限个数的定类变量。最常见的是二分类模型。</p></li>
|
||
</ul>
|
||
<p>我们主要使用波士顿房价数据集来实现各种模型。因此我们使用2021作为种子值生成70%的数据作为训练集，其余数据作为测试集。下面展示来各个数据集的大小。</p>
|
||
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="co"># 导入BostonHousing数据</span></span>
|
||
<span id="cb1-2"><a href="#cb1-2"></a><span class="kw">library</span>(mlbench)</span>
|
||
<span id="cb1-3"><a href="#cb1-3"></a><span class="kw">data</span>(BostonHousing)</span>
|
||
<span id="cb1-4"><a href="#cb1-4"></a></span>
|
||
<span id="cb1-5"><a href="#cb1-5"></a><span class="co"># 设置种子值，方便复现</span></span>
|
||
<span id="cb1-6"><a href="#cb1-6"></a><span class="kw">set.seed</span>(<span class="dv">2021</span>)</span>
|
||
<span id="cb1-7"><a href="#cb1-7"></a></span>
|
||
<span id="cb1-8"><a href="#cb1-8"></a><span class="co"># 生成训练集的索引，用来划分训练集和测试集</span></span>
|
||
<span id="cb1-9"><a href="#cb1-9"></a>train_index <-<span class="st"> </span><span class="kw">sample</span>(<span class="kw">dim</span>(BostonHousing)[<span class="dv">1</span>], <span class="fl">0.7</span> <span class="op">*</span><span class="st"> </span><span class="kw">dim</span>(BostonHousing)[<span class="dv">1</span>])</span>
|
||
<span id="cb1-10"><a href="#cb1-10"></a>BostonHousingTrain <-<span class="st"> </span>BostonHousing[train_index, ]</span>
|
||
<span id="cb1-11"><a href="#cb1-11"></a>BostonHousingTest <-<span class="st"> </span>BostonHousing[<span class="op">-</span>train_index, ]</span>
|
||
<span id="cb1-12"><a href="#cb1-12"></a></span>
|
||
<span id="cb1-13"><a href="#cb1-13"></a><span class="co"># 查看数据集的size</span></span>
|
||
<span id="cb1-14"><a href="#cb1-14"></a><span class="kw">dim</span>(BostonHousing)</span></code></pre></div>
|
||
<pre><code>## [1] 506 14</code></pre>
|
||
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a><span class="kw">dim</span>(BostonHousingTrain)</span></code></pre></div>
|
||
<pre><code>## [1] 354 14</code></pre>
|
||
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1"></a><span class="kw">dim</span>(BostonHousingTest)</span></code></pre></div>
|
||
<pre><code>## [1] 152 14</code></pre>
|
||
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a><span class="co"># 查看数据集包含的变量名称</span></span>
|
||
<span id="cb7-2"><a href="#cb7-2"></a><span class="kw">names</span>(BostonHousing)</span></code></pre></div>
|
||
<pre><code>## [1] "crim" "zn" "indus" "chas" "nox" "rm" "age"
|
||
## [8] "dis" "rad" "tax" "ptratio" "b" "lstat" "medv"</code></pre>
|
||
</div>
|
||
<div id="回归模型" class="section level2">
|
||
<h2>2 回归模型</h2>
|
||
<p>回归模型有很多主要有Linear Regression、Logistic Regression、Polynomial Regression、Stepwise Regression、Ridge Regression、Lasso Regression、ElasticNet等。</p>
|
||
<p>本部分主要介绍有Linear Regression、以及Stepwise Regression三种回归模型的实现。</p>
|
||
<div id="linear-regression" class="section level3">
|
||
<h3>2.1 Linear Regression</h3>
|
||
<p>多元线性回归是一种最为基础的回归模型，其使用多个自变量和一个因变量利用OLS完成模型训练。下面我们将使用<code>medv</code>作为因变量，剩余变量作为自变量构建模型。</p>
|
||
<p>多元线性回归模型使用<code>lm()</code>命令, 其中<code>medv~.</code>是回归公式，<code>data=BostonHousingTrain</code>是回归数据。对回归公式的构建进行一些补充，<code>~</code>左侧表示因变量，<code>~</code>右侧表示自变量，多个自变量使用<code>+</code>依次叠加。这里右侧使用了<code>.</code>，该符号的含义是除左侧变量外所有的变量。因此，<code>medv~.</code>等价于<code>medv~crim + zn + indus + chas + nox + rm + age + dis + rad + tax + ptratio + b + medv</code>。</p>
|
||
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1"></a><span class="co"># 构建模型，medv~.表示回归方程</span></span>
|
||
<span id="cb9-2"><a href="#cb9-2"></a>lr_model <-<span class="st"> </span><span class="kw">lm</span>(medv <span class="op">~</span><span class="st"> </span>., <span class="dt">data =</span> BostonHousingTrain)</span>
|
||
<span id="cb9-3"><a href="#cb9-3"></a></span>
|
||
<span id="cb9-4"><a href="#cb9-4"></a><span class="co"># summary输出模型汇总</span></span>
|
||
<span id="cb9-5"><a href="#cb9-5"></a><span class="kw">summary</span>(lr_model)</span></code></pre></div>
|
||
<pre><code>##
|
||
## Call:
|
||
## lm(formula = medv ~ ., data = BostonHousingTrain)
|
||
##
|
||
## Residuals:
|
||
## Min 1Q Median 3Q Max
|
||
## -17.1929 -2.6567 -0.3854 1.6261 28.5425
|
||
##
|
||
## Coefficients:
|
||
## Estimate Std. Error t value Pr(>|t|)
|
||
## (Intercept) 28.279554 6.464743 4.374 1.62e-05 ***
|
||
## crim -0.066574 0.051496 -1.293 0.196958
|
||
## zn 0.031466 0.016525 1.904 0.057733 .
|
||
## indus 0.046583 0.069009 0.675 0.500115
|
||
## chas1 3.372501 1.065312 3.166 0.001687 **
|
||
## nox -14.103937 4.498414 -3.135 0.001866 **
|
||
## rm 4.512687 0.547845 8.237 3.85e-15 ***
|
||
## age -0.010015 0.016016 -0.625 0.532197
|
||
## dis -1.259008 0.245311 -5.132 4.82e-07 ***
|
||
## rad 0.263841 0.077147 3.420 0.000702 ***
|
||
## tax -0.012026 0.004176 -2.880 0.004235 **
|
||
## ptratio -1.008997 0.160048 -6.304 8.99e-10 ***
|
||
## b 0.014361 0.003406 4.217 3.18e-05 ***
|
||
## lstat -0.466948 0.062026 -7.528 4.66e-13 ***
|
||
## ---
|
||
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
|
||
##
|
||
## Residual standard error: 4.776 on 340 degrees of freedom
|
||
## Multiple R-squared: 0.7299, Adjusted R-squared: 0.7196
|
||
## F-statistic: 70.67 on 13 and 340 DF, p-value: < 2.2e-16</code></pre>
|
||
<p>运用plot命令对模型进行诊断，各图含义参考 <a href="https://www.cnblogs.com/lafengdatascientist/p/5554167.html" class="uri">https://www.cnblogs.com/lafengdatascientist/p/5554167.html</a></p>
|
||
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1"></a><span class="kw">plot</span>(lr_model)</span></code></pre></div>
|
||
<p><img src="data:image/png;base64,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" 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" 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" 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" /><!-- --></p>
|
||
<p><code>predict</code>命令能够基于已经训练好的模型进行预测。</p>
|
||
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1"></a><span class="co"># 根据模型对新数据进行预测</span></span>
|
||
<span id="cb12-2"><a href="#cb12-2"></a>BostonHousingTest<span class="op">$</span>lr_pred <-<span class="st"> </span><span class="kw">predict</span>(lr_model, <span class="dt">newdata =</span> BostonHousingTest)</span></code></pre></div>
|
||
</div>
|
||
<div id="stepwise-regression" class="section level3">
|
||
<h3>2.2 Stepwise Regression</h3>
|
||
<p>利用逐步回归分析可以对模型中的变量进行优化。R语言中的<code>step()</code>命令,是以AIC信息统计量为准则，通过选择最小的AIC信息统计量来达到提出或添加变量的目的。</p>
|
||
<p>对于逐步回归，一般有前向、后向、双向等逐步方式。本部分将基于已经实现的<code>lr_model</code>进行双向逐步回归。前向和后向回归只需要更改<code>step()</code>命令行中的<code>direstion</code>参数即可。具体内容参照 <a href="https://blog.csdn.net/qq_38204302/article/details/86567356" class="uri">https://blog.csdn.net/qq_38204302/article/details/86567356</a></p>
|
||
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1"></a><span class="co"># both逐步回归</span></span>
|
||
<span id="cb13-2"><a href="#cb13-2"></a>step_model <-<span class="st"> </span><span class="kw">step</span>(lr_model, <span class="dt">direction =</span> <span class="st">"both"</span>)</span></code></pre></div>
|
||
<pre><code>## Start: AIC=1120.78
|
||
## medv ~ crim + zn + indus + chas + nox + rm + age + dis + rad +
|
||
## tax + ptratio + b + lstat
|
||
##
|
||
## Df Sum of Sq RSS AIC
|
||
## - age 1 8.92 7765.1 1119.2
|
||
## - indus 1 10.39 7766.6 1119.3
|
||
## - crim 1 38.13 7794.3 1120.5
|
||
## <none> 7756.2 1120.8
|
||
## - zn 1 82.71 7838.9 1122.5
|
||
## - tax 1 189.16 7945.4 1127.3
|
||
## - nox 1 224.25 7980.5 1128.9
|
||
## - chas 1 228.62 7984.8 1129.1
|
||
## - rad 1 266.82 8023.0 1130.8
|
||
## - b 1 405.60 8161.8 1136.8
|
||
## - dis 1 600.89 8357.1 1145.2
|
||
## - ptratio 1 906.67 8662.9 1157.9
|
||
## - lstat 1 1292.88 9049.1 1173.4
|
||
## - rm 1 1547.84 9304.0 1183.2
|
||
##
|
||
## Step: AIC=1119.19
|
||
## medv ~ crim + zn + indus + chas + nox + rm + dis + rad + tax +
|
||
## ptratio + b + lstat
|
||
##
|
||
## Df Sum of Sq RSS AIC
|
||
## - indus 1 10.22 7775.3 1117.7
|
||
## - crim 1 39.31 7804.4 1119.0
|
||
## <none> 7765.1 1119.2
|
||
## + age 1 8.92 7756.2 1120.8
|
||
## - zn 1 92.34 7857.5 1121.4
|
||
## - tax 1 193.70 7958.8 1125.9
|
||
## - chas 1 225.98 7991.1 1127.3
|
||
## - nox 1 261.86 8027.0 1128.9
|
||
## - rad 1 278.77 8043.9 1129.7
|
||
## - b 1 398.83 8164.0 1134.9
|
||
## - dis 1 613.30 8378.4 1144.1
|
||
## - ptratio 1 916.06 8681.2 1156.7
|
||
## - lstat 1 1546.55 9311.7 1181.5
|
||
## - rm 1 1571.42 9336.5 1182.4
|
||
##
|
||
## Step: AIC=1117.65
|
||
## medv ~ crim + zn + chas + nox + rm + dis + rad + tax + ptratio +
|
||
## b + lstat
|
||
##
|
||
## Df Sum of Sq RSS AIC
|
||
## - crim 1 41.19 7816.5 1117.5
|
||
## <none> 7775.3 1117.7
|
||
## + indus 1 10.22 7765.1 1119.2
|
||
## + age 1 8.74 7766.6 1119.3
|
||
## - zn 1 88.58 7863.9 1119.7
|
||
## - tax 1 189.88 7965.2 1124.2
|
||
## - chas 1 231.63 8007.0 1126.0
|
||
## - nox 1 252.32 8027.7 1127.0
|
||
## - rad 1 269.59 8044.9 1127.7
|
||
## - b 1 395.78 8171.1 1133.2
|
||
## - dis 1 706.93 8482.3 1146.5
|
||
## - ptratio 1 906.25 8681.6 1154.7
|
||
## - lstat 1 1537.69 9313.0 1179.5
|
||
## - rm 1 1561.38 9336.7 1180.4
|
||
##
|
||
## Step: AIC=1117.52
|
||
## medv ~ zn + chas + nox + rm + dis + rad + tax + ptratio + b +
|
||
## lstat
|
||
##
|
||
## Df Sum of Sq RSS AIC
|
||
## <none> 7816.5 1117.5
|
||
## + crim 1 41.19 7775.3 1117.7
|
||
## + indus 1 12.10 7804.4 1119.0
|
||
## - zn 1 76.92 7893.5 1119.0
|
||
## + age 1 9.92 7806.6 1119.1
|
||
## - tax 1 182.40 7998.9 1123.7
|
||
## - rad 1 228.86 8045.4 1125.7
|
||
## - nox 1 236.90 8053.4 1126.1
|
||
## - chas 1 240.06 8056.6 1126.2
|
||
## - b 1 514.43 8331.0 1138.1
|
||
## - dis 1 673.74 8490.3 1144.8
|
||
## - ptratio 1 893.27 8709.8 1153.8
|
||
## - lstat 1 1589.98 9406.5 1181.1
|
||
## - rm 1 1636.60 9453.1 1182.8</code></pre>
|
||
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1"></a><span class="kw">summary</span>(step_model)</span></code></pre></div>
|
||
<pre><code>##
|
||
## Call:
|
||
## lm(formula = medv ~ zn + chas + nox + rm + dis + rad + tax +
|
||
## ptratio + b + lstat, data = BostonHousingTrain)
|
||
##
|
||
## Residuals:
|
||
## Min 1Q Median 3Q Max
|
||
## -16.8955 -2.6773 -0.4005 1.6707 28.5842
|
||
##
|
||
## Coefficients:
|
||
## Estimate Std. Error t value Pr(>|t|)
|
||
## (Intercept) 27.001770 6.354437 4.249 2.77e-05 ***
|
||
## zn 0.029797 0.016219 1.837 0.06705 .
|
||
## chas1 3.446516 1.061891 3.246 0.00129 **
|
||
## nox -13.578105 4.211269 -3.224 0.00138 **
|
||
## rm 4.491255 0.529976 8.474 7.07e-16 ***
|
||
## dis -1.213451 0.223170 -5.437 1.03e-07 ***
|
||
## rad 0.220392 0.069546 3.169 0.00167 **
|
||
## tax -0.010818 0.003824 -2.829 0.00494 **
|
||
## ptratio -0.991885 0.158427 -6.261 1.14e-09 ***
|
||
## b 0.015446 0.003251 4.751 2.98e-06 ***
|
||
## lstat -0.482234 0.057733 -8.353 1.67e-15 ***
|
||
## ---
|
||
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
|
||
##
|
||
## Residual standard error: 4.774 on 343 degrees of freedom
|
||
## Multiple R-squared: 0.7278, Adjusted R-squared: 0.7199
|
||
## F-statistic: 91.71 on 10 and 343 DF, p-value: < 2.2e-16</code></pre>
|
||
<p>对于分类模型还有较为常用的Lasso Regression 和 Ridge Regression，我们将会在进阶教程中来更加具体的讲解模型知识。</p>
|
||
</div>
|
||
</div>
|
||
<div id="分类模型" class="section level2">
|
||
<h2>3 分类模型</h2>
|
||
<p>在进行分类模型前，我们需要构建分类标签。我们使用<code>medv</code>的中位数进行划分，其中1表示高房价，0表示低房价。通过这样的转化将原本的数值型变量转化为二元标签。并使用相同的种子值划分测试集和训练集。</p>
|
||
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1"></a><span class="co"># 将连续变量转化成二分类变量</span></span>
|
||
<span id="cb17-2"><a href="#cb17-2"></a>BostonHousing<span class="op">$</span>medv <-<span class="st"> </span><span class="kw">as.factor</span>(<span class="kw">ifelse</span>(BostonHousing<span class="op">$</span>medv <span class="op">></span><span class="st"> </span><span class="kw">median</span>(BostonHousing<span class="op">$</span>medv),</span>
|
||
<span id="cb17-3"><a href="#cb17-3"></a> <span class="dv">1</span>, <span class="dv">0</span>))</span>
|
||
<span id="cb17-4"><a href="#cb17-4"></a><span class="co"># 查看两种变量类别的数量</span></span>
|
||
<span id="cb17-5"><a href="#cb17-5"></a><span class="kw">summary</span>(BostonHousing<span class="op">$</span>medv)</span></code></pre></div>
|
||
<pre><code>## 0 1
|
||
## 256 250</code></pre>
|
||
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1"></a><span class="co"># 使用相同的种子值，复现训练集合测试集的划分</span></span>
|
||
<span id="cb19-2"><a href="#cb19-2"></a><span class="kw">set.seed</span>(<span class="dv">2021</span>)</span>
|
||
<span id="cb19-3"><a href="#cb19-3"></a>train_index <-<span class="st"> </span><span class="kw">sample</span>(<span class="kw">dim</span>(BostonHousing)[<span class="dv">1</span>], <span class="fl">0.7</span> <span class="op">*</span><span class="st"> </span><span class="kw">dim</span>(BostonHousing)[<span class="dv">1</span>])</span>
|
||
<span id="cb19-4"><a href="#cb19-4"></a>BostonHousingTrain <-<span class="st"> </span>BostonHousing[train_index, ]</span>
|
||
<span id="cb19-5"><a href="#cb19-5"></a>BostonHousingTest <-<span class="st"> </span>BostonHousing[<span class="op">-</span>train_index, ]</span></code></pre></div>
|
||
<p>同时引入两个计算函数，用来计算AUC指标值。</p>
|
||
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1"></a><span class="co"># 引入auc计算函数</span></span>
|
||
<span id="cb20-2"><a href="#cb20-2"></a><span class="kw">library</span>(<span class="st">"ROCR"</span>)</span>
|
||
<span id="cb20-3"><a href="#cb20-3"></a>calcAUC <-<span class="st"> </span><span class="cf">function</span>(predcol, outcol) {</span>
|
||
<span id="cb20-4"><a href="#cb20-4"></a> perf <-<span class="st"> </span><span class="kw">performance</span>(<span class="kw">prediction</span>(predcol, outcol <span class="op">==</span><span class="st"> </span><span class="dv">1</span>), <span class="st">"auc"</span>)</span>
|
||
<span id="cb20-5"><a href="#cb20-5"></a> <span class="kw">as.numeric</span>(perf<span class="op">@</span>y.values)</span>
|
||
<span id="cb20-6"><a href="#cb20-6"></a>}</span></code></pre></div>
|
||
<div id="logistics-regression" class="section level3">
|
||
<h3>3.1 Logistics Regression</h3>
|
||
<p>逻辑回归是一种广义的线性回归分析模型，利用sigmode将线性回归结果转化成概率的形式。下面展示了利用<code>glm()</code>构建逻辑回归的过程。通过计算，训练集上的auc取值为0.9554211，测试集上的auc取值为0.9506969，说明模型效果整体不错。</p>
|
||
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1"></a><span class="co"># 逻辑回归模型构建</span></span>
|
||
<span id="cb21-2"><a href="#cb21-2"></a>lr_model <-<span class="st"> </span><span class="kw">glm</span>(medv <span class="op">~</span><span class="st"> </span>., <span class="dt">data =</span> BostonHousingTrain, <span class="dt">family =</span> <span class="kw">binomial</span>(<span class="dt">link =</span> <span class="st">"logit"</span>))</span>
|
||
<span id="cb21-3"><a href="#cb21-3"></a><span class="kw">summary</span>(lr_model)</span></code></pre></div>
|
||
<pre><code>##
|
||
## Call:
|
||
## glm(formula = medv ~ ., family = binomial(link = "logit"), data = BostonHousingTrain)
|
||
##
|
||
## Deviance Residuals:
|
||
## Min 1Q Median 3Q Max
|
||
## -2.00065 -0.34945 -0.01094 0.24116 3.00080
|
||
##
|
||
## Coefficients:
|
||
## Estimate Std. Error z value Pr(>|z|)
|
||
## (Intercept) 4.641164 4.937497 0.940 0.347226
|
||
## crim -0.053419 0.096982 -0.551 0.581760
|
||
## zn 0.005680 0.015218 0.373 0.708951
|
||
## indus 0.045677 0.048167 0.948 0.342973
|
||
## chas1 1.634949 0.798937 2.046 0.040717 *
|
||
## nox -6.916586 3.286514 -2.105 0.035332 *
|
||
## rm 2.876778 0.651573 4.415 1.01e-05 ***
|
||
## age -0.034146 0.013493 -2.531 0.011383 *
|
||
## dis -0.696695 0.209391 -3.327 0.000877 ***
|
||
## rad 0.220168 0.074211 2.967 0.003009 **
|
||
## tax -0.009724 0.003446 -2.822 0.004769 **
|
||
## ptratio -0.611081 0.132894 -4.598 4.26e-06 ***
|
||
## b 0.006135 0.003830 1.602 0.109159
|
||
## lstat -0.267857 0.064765 -4.136 3.54e-05 ***
|
||
## ---
|
||
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
|
||
##
|
||
## (Dispersion parameter for binomial family taken to be 1)
|
||
##
|
||
## Null deviance: 489.83 on 353 degrees of freedom
|
||
## Residual deviance: 187.85 on 340 degrees of freedom
|
||
## AIC: 215.85
|
||
##
|
||
## Number of Fisher Scoring iterations: 7</code></pre>
|
||
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1"></a><span class="co"># 分别对训练集和测试集进行预测</span></span>
|
||
<span id="cb23-2"><a href="#cb23-2"></a>lr_pred_train <-<span class="st"> </span><span class="kw">predict</span>(lr_model, <span class="dt">newdata =</span> BostonHousingTrain, <span class="dt">type =</span> <span class="st">"response"</span>)</span>
|
||
<span id="cb23-3"><a href="#cb23-3"></a>lr_pred_test <-<span class="st"> </span><span class="kw">predict</span>(lr_model, <span class="dt">newdata =</span> BostonHousingTest, <span class="dt">type =</span> <span class="st">"response"</span>)</span>
|
||
<span id="cb23-4"><a href="#cb23-4"></a></span>
|
||
<span id="cb23-5"><a href="#cb23-5"></a><span class="co"># 计算训练集和测试集的auc</span></span>
|
||
<span id="cb23-6"><a href="#cb23-6"></a><span class="kw">calcAUC</span>(lr_pred_train, BostonHousingTrain<span class="op">$</span>medv)</span></code></pre></div>
|
||
<pre><code>## [1] 0.9554211</code></pre>
|
||
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1"></a><span class="kw">calcAUC</span>(lr_pred_test, BostonHousingTest<span class="op">$</span>medv)</span></code></pre></div>
|
||
<pre><code>## [1] 0.9506969</code></pre>
|
||
</div>
|
||
<div id="knn" class="section level3">
|
||
<h3>3.2 KNN</h3>
|
||
<p>KNN模型是一种简单易懂、可以用于分类和回归的模型。其中 K 表示在新样本点附近(距离)选取 K 个样本数据，通过在 K 个样本进行投票来判断新增样本的类型。</p>
|
||
<p>KNN模型较难的一点是确定超参数K，目前有一些指标和经验方法帮助确定最优K的取值。这部分内容会在后续进行讲解，这里使用k=25进行建模。KNN模型在测试集上的auc值为0.8686411，相比于逻辑回归效果较差。</p>
|
||
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1"></a><span class="co"># 导入knn模型的包</span></span>
|
||
<span id="cb27-2"><a href="#cb27-2"></a><span class="kw">library</span>(kknn)</span>
|
||
<span id="cb27-3"><a href="#cb27-3"></a></span>
|
||
<span id="cb27-4"><a href="#cb27-4"></a><span class="co"># 构建knn模型</span></span>
|
||
<span id="cb27-5"><a href="#cb27-5"></a>knn <-<span class="st"> </span><span class="kw">kknn</span>(medv <span class="op">~</span><span class="st"> </span>., BostonHousingTrain, BostonHousingTest, <span class="dt">k =</span> <span class="dv">25</span>)</span>
|
||
<span id="cb27-6"><a href="#cb27-6"></a></span>
|
||
<span id="cb27-7"><a href="#cb27-7"></a><span class="co"># 预测并计算测试集上的auc取值</span></span>
|
||
<span id="cb27-8"><a href="#cb27-8"></a>knn_pred_test <-<span class="st"> </span><span class="kw">predict</span>(knn, <span class="dt">newdata =</span> BostonHousingTest)</span>
|
||
<span id="cb27-9"><a href="#cb27-9"></a><span class="kw">calcAUC</span>(<span class="kw">as.numeric</span>(knn_pred_test), BostonHousingTest<span class="op">$</span>medv)</span></code></pre></div>
|
||
<pre><code>## [1] 0.875784</code></pre>
|
||
</div>
|
||
<div id="decision-tree" class="section level3">
|
||
<h3>3.3 Decision Tree</h3>
|
||
<p>决策树是一种基于树模型进行划分的分类模型，通过一系列if then决策规则的集合，将特征空间划分成有限个不相交的子区域，对于落在相同子区域的样本，决策树模型给出相同的预测值。下面构建了决策树的分类模型</p>
|
||
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1"></a><span class="co"># 导入包</span></span>
|
||
<span id="cb29-2"><a href="#cb29-2"></a><span class="kw">library</span>(tree)</span>
|
||
<span id="cb29-3"><a href="#cb29-3"></a></span>
|
||
<span id="cb29-4"><a href="#cb29-4"></a><span class="co"># 构建决策树模型函数，medv~.是决策树公式，用来表明变量。</span></span>
|
||
<span id="cb29-5"><a href="#cb29-5"></a><span class="co"># summary输出模型汇总信息</span></span>
|
||
<span id="cb29-6"><a href="#cb29-6"></a>dt_model <-<span class="st"> </span><span class="kw">tree</span>(medv <span class="op">~</span><span class="st"> </span>., BostonHousingTrain)</span>
|
||
<span id="cb29-7"><a href="#cb29-7"></a><span class="kw">summary</span>(dt_model)</span></code></pre></div>
|
||
<pre><code>##
|
||
## Classification tree:
|
||
## tree(formula = medv ~ ., data = BostonHousingTrain)
|
||
## Variables actually used in tree construction:
|
||
## [1] "lstat" "rm" "crim" "ptratio" "b" "tax" "dis"
|
||
## [8] "age" "nox" "zn"
|
||
## Number of terminal nodes: 20
|
||
## Residual mean deviance: 0.2984 = 99.66 / 334
|
||
## Misclassification error rate: 0.07062 = 25 / 354</code></pre>
|
||
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1"></a><span class="co"># plot可以对树模型进行绘制，但可能会出现书分支过多的情况。</span></span>
|
||
<span id="cb31-2"><a href="#cb31-2"></a><span class="kw">plot</span>(dt_model)</span>
|
||
<span id="cb31-3"><a href="#cb31-3"></a><span class="kw">text</span>(dt_model)</span></code></pre></div>
|
||
<p><img 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" /><!-- --></p>
|
||
<p>在构建决策树模型的基础上，分别对训练集和测试集进行预测并计算auc取值。该模型在训练集上的auc取值为0.9281874，在测试集上的auc取值为0.8789199。训练集和测试集间存在抖动，说明该模型可能出现过拟合。我们需要引入剪枝的操作来降低模型的过拟合，这部分供同学们自学。</p>
|
||
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1"></a><span class="co"># 预测</span></span>
|
||
<span id="cb32-2"><a href="#cb32-2"></a>dt_pred_train <-<span class="st"> </span><span class="kw">predict</span>(dt_model, <span class="dt">newdata =</span> BostonHousingTrain, <span class="dt">type =</span> <span class="st">"class"</span>)</span>
|
||
<span id="cb32-3"><a href="#cb32-3"></a>dt_pred_test <-<span class="st"> </span><span class="kw">predict</span>(dt_model, <span class="dt">newdata =</span> BostonHousingTest, <span class="dt">type =</span> <span class="st">"class"</span>)</span>
|
||
<span id="cb32-4"><a href="#cb32-4"></a></span>
|
||
<span id="cb32-5"><a href="#cb32-5"></a><span class="co"># 计算auc取值</span></span>
|
||
<span id="cb32-6"><a href="#cb32-6"></a><span class="kw">calcAUC</span>(<span class="kw">as.numeric</span>(dt_pred_train), BostonHousingTrain<span class="op">$</span>medv)</span></code></pre></div>
|
||
<pre><code>## [1] 0.9308756</code></pre>
|
||
<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1"></a><span class="kw">calcAUC</span>(<span class="kw">as.numeric</span>(dt_pred_test), BostonHousingTest<span class="op">$</span>medv)</span></code></pre></div>
|
||
<pre><code>## [1] 0.8789199</code></pre>
|
||
</div>
|
||
<div id="random-forest" class="section level3">
|
||
<h3>3.4 Random Forest</h3>
|
||
<p>随机森林是一个包含多个决策树的分类器，可以用于分类和回归问题。在解决分类问题是，其输出的类别是由个别树输出的类别的众数而定。相比于单树模型，随机森林具有更好地泛化能力。</p>
|
||
<p>使用<code>randomForest()</code>构建模型的过程中，可以通过<code>ntree</code>设定随机森林中包含的决策树数量。由于随机森林是对样本和变量的随机，因此可以通过<code>important</code>展示变量的重要性排序。通过模型预测，随机森林模型在训练集上的auc为0.9615975，在测试集上的auc为0.9247387。</p>
|
||
<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1"></a><span class="co"># 导入随机森林包</span></span>
|
||
<span id="cb36-2"><a href="#cb36-2"></a><span class="kw">library</span>(randomForest)</span>
|
||
<span id="cb36-3"><a href="#cb36-3"></a></span>
|
||
<span id="cb36-4"><a href="#cb36-4"></a><span class="co"># 随机森林模型</span></span>
|
||
<span id="cb36-5"><a href="#cb36-5"></a>rf_model <-<span class="st"> </span><span class="kw">randomForest</span>(medv <span class="op">~</span><span class="st"> </span>., BostonHousingTrain, <span class="dt">ntree =</span> <span class="dv">100</span>, <span class="dt">nodesize =</span> <span class="dv">10</span>,</span>
|
||
<span id="cb36-6"><a href="#cb36-6"></a> <span class="dt">importance =</span> T)</span>
|
||
<span id="cb36-7"><a href="#cb36-7"></a><span class="co"># 展示模型变量的重要性</span></span>
|
||
<span id="cb36-8"><a href="#cb36-8"></a><span class="kw">importance</span>(rf_model)</span></code></pre></div>
|
||
<pre><code>## 0 1 MeanDecreaseAccuracy MeanDecreaseGini
|
||
## crim 3.0460631 1.5455430 3.9486776 5.762997
|
||
## zn 3.1035729 1.5721594 3.6238915 1.886801
|
||
## indus 3.8338867 1.4335357 4.6616469 7.176498
|
||
## chas 1.6703290 -1.5235785 0.7998773 1.100619
|
||
## nox 4.6899935 4.2616418 6.3944503 16.005287
|
||
## rm 11.0161057 10.2260377 14.5799077 24.681409
|
||
## age 5.6799908 3.3897131 6.9069090 9.107270
|
||
## dis 4.2225512 3.8567841 6.1001670 8.419924
|
||
## rad 0.9290789 -0.3819842 0.8369308 1.449089
|
||
## tax 1.1409763 7.2597262 7.5416998 8.688504
|
||
## ptratio 3.4528462 5.8912306 6.5636512 11.890037
|
||
## b -0.4174669 4.4680208 3.3717663 3.990056
|
||
## lstat 14.5324793 12.5910741 18.7108835 44.289292</code></pre>
|
||
<div class="sourceCode" id="cb38"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1"></a><span class="co"># 预测</span></span>
|
||
<span id="cb38-2"><a href="#cb38-2"></a>rf_pred_train <-<span class="st"> </span><span class="kw">predict</span>(rf_model, <span class="dt">newdata =</span> BostonHousingTrain, <span class="dt">type =</span> <span class="st">"class"</span>)</span>
|
||
<span id="cb38-3"><a href="#cb38-3"></a>rf_pred_test <-<span class="st"> </span><span class="kw">predict</span>(rf_model, <span class="dt">newdata =</span> BostonHousingTest, <span class="dt">type =</span> <span class="st">"class"</span>)</span>
|
||
<span id="cb38-4"><a href="#cb38-4"></a></span>
|
||
<span id="cb38-5"><a href="#cb38-5"></a><span class="co"># 计算auc取值</span></span>
|
||
<span id="cb38-6"><a href="#cb38-6"></a><span class="kw">calcAUC</span>(<span class="kw">as.numeric</span>(rf_pred_train), BostonHousingTrain<span class="op">$</span>medv)</span></code></pre></div>
|
||
<pre><code>## [1] 0.9675499</code></pre>
|
||
<div class="sourceCode" id="cb40"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1"></a><span class="kw">calcAUC</span>(<span class="kw">as.numeric</span>(rf_pred_test), BostonHousingTest<span class="op">$</span>medv)</span></code></pre></div>
|
||
<pre><code>## [1] 0.9236934</code></pre>
|
||
</div>
|
||
</div>
|
||
<div id="思考与练习" class="section level2">
|
||
<h2>思考与练习</h2>
|
||
<p>本章节仅对模型进行简单介绍，更多详细、复杂的模型将在后面的进阶课程中展开。</p>
|
||
<p>学习完本章节，希望你能够尝试一些模型调优工作。如决策树剪枝，如尝试搜索KNN模型中最佳K取值等。</p>
|
||
<p><strong>Task5 END.</strong></p>
|
||
<p>— By: 张晋</p>
|
||
<blockquote>
|
||
<p>Datawhale成员, 数据科学爱好者</p>
|
||
</blockquote>
|
||
<p>关于Datawhale： Datawhale是一个专注于数据科学与AI领域的开源组织，汇集了众多领域院校和知名企业的优秀学习者，聚合了一群有开源精神和探索精神的团队成员。Datawhale 以“for the learner，和学习者一起成长”为愿景，鼓励真实地展现自我、开放包容、互信互助、敢于试错和勇于担当。同时 Datawhale 用开源的理念去探索开源内容、开源学习和开源方案，赋能人才培养，助力人才成长，建立起人与人，人与知识，人与企业和人与未来的联结。 本次数据挖掘路径学习，专题知识将在天池分享，详情可关注Datawhale：</p>
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<p><a href="https://camo.githubusercontent.com/8578ee173c78b587d5058439bbd0b98fa39c173def229a8c3d957e62aac0b649/68747470733a2f2f696d672d626c6f672e6373646e696d672e636e2f323032303039313330313032323639382e706e67237069635f63656e746572"><img 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