{"id":580,"date":"2020-11-30T15:21:41","date_gmt":"2020-11-30T06:21:41","guid":{"rendered":"http:\/\/cedartrees.co.kr\/?p=580"},"modified":"2021-01-09T19:23:31","modified_gmt":"2021-01-09T10:23:31","slug":"sklearn-datasets","status":"publish","type":"post","link":"http:\/\/blog.cedartrees.co.kr\/index.php\/2020\/11\/30\/sklearn-datasets\/","title":{"rendered":"sklearn.datasets"},"content":{"rendered":"\n

Load_Boston \ub370\uc774\ud130\uc14b<\/span><\/strong><\/h4>\n\n\n\n

sklearn\uc5d0\uc11c\ub294 \uac04\ub2e8\ud55c \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ubd84\uc11d\uacfc \ud14c\uc2a4\ud2b8\ub97c \uc704\ud574\uc11c \uc791\uc740 \uaddc\ubaa8\uc758 \ub370\uc774\ud130\uc14b(small toy datasets)\uc744 datasets\uc774\ub77c\ub294 \ud328\ud0a4\uc9c0\uc5d0 \ub2f4\uc544\ub450\uc5c8\uc2b5\ub2c8\ub2e4. \ud574\ub2f9 \ub370\uc774\ud130\uc14b\ub4e4\uc740 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc815\ubcf4\uc640 \ud2b9\uc9d5\ub4e4\uc774 \ud3ec\ud568\ub418\uc5b4 \uc788\uc5b4 \ub370\uc774\ud130\uc14b\uc744 \uc774\uc6a9\ud558\ub294 \uc0ac\uc6a9\uc790\ub85c \ud558\uc5ec\uae08 \ud574\ub2f9 \ub370\uc774\ud130\uc14b\uc774 \uc5b4\ub5a4 \uc758\ubbf8\uc778\uac00\ub97c \uc27d\uac8c \uc774\ud574\ud560 \uc218 \uc788\ub3c4\ub85d \ud574\uc8fc\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n\n\n\n

\"\"
https:\/\/scikit-learn.org\/stable\/datasets\/index.html#toy-datasets<\/figcaption><\/figure><\/div>\n\n\n\n

\uacf5\uc2dd \ud648\ud398\uc774\uc9c0\uc5d0 \ub4e4\uc5b4\uac00\ubcf4\uc2dc\uba74 \uc81c\uacf5\ud558\ub294 \ub370\uc774\ud130\uc758 \ub0b4\uc6a9\ub4e4\uc744 \ud655\uc778 \ud558\uc2e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uac04\ub7b5\ud55c \uc815\ubcf4\ub294 \uc544\ub798\uc640 \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n\n\n\n

  • load_boston : \ubcf4\uc2a4\ud134 \uc9c0\uc5ed\uc758 \uc9d1\uac12 \ub370\uc774\ud130(\ud68c\uadc0\ubd84\uc11d)<\/li>
  • load_iris : \ubd97\uaf43 \ub370\uc774\ud130(\ubd84\ub958)<\/li>
  • load_diabetes : \ub2f9\ub1e8\ubcd1 \ub370\uc774\ud130(\ud68c\uadc0\ubd84\uc11d)<\/li>
  • load_digits : \uc22b\uc790 \uc774\ubbf8\uc9c0 \ub370\uc774\ud130(\ubd84\ub958)<\/li>
  • load_linnerud : 20\ub300 \uc911\ubc18 \ub0a8\uc131\uc758 \uc2e0\uccb4\uc815\ubcf4\uc640 \uc6b4\ub3d9\uc815\ubcf4(\ub2e4\ubcc0\ub7c9\ubd84\uc11d)<\/li>
  • load_wine : \uc640\uc778\uc758 \ud2b9\uc9d5\uc5d0 \ub530\ub978 \uc885\ub958(\ubd84\ub958)<\/li>
  • load_breast_cancer : \uc720\ubc29\uc554 \ub370\uc774\ud130(\ubd84\ub958)<\/li><\/ul>\n\n\n\n
    from sklearn.datasets import load_boston\nboston = load_boston()<\/pre>\n\n\n\n
    \ub370\uc774\ud130\uc14b\uc744 \ub85c\ub529\ud558\ub294\ub370\ub294 load\ub77c\ub294 \uba85\ub839\uc5b4\uc640 \ud568\uaed8 \ud574\ub2f9 \ub370\uc774\ud130\uc14b\uc758 \uc774\ub984\uc744 \uc801\uc5b4\uc8fc\uba74 \uac04\ub2e8\ud788 \ub85c\ub529\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.
    \ub370\uc774\ud130\ub97c \ub85c\ub529\ud55c \ud6c4\uc5d0\ub294 DESCR \uc744 \ud1b5\ud574\uc11c \ud574\ub2f9 \ub370\uc774\ud130\uc14b\uc774 \uc5b4\ub5a4 \ud2b9\uc9d5\uc774 \uc788\ub294\uc9c0 \uc0b4\ud3b4\ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n\n\n\n
    print(boston.DESCR)<\/pre>\n\n\n\n
    .. _boston_dataset:\n\nBoston house prices dataset\n---------------------------\n\n**Data Set Characteristics:**  \n\n    :Number of Instances: 506 \n\n    :Number of Attributes: 13 numeric\/categorical predictive. Median Value (attribute 14) is usually the target.\n\n    :Attribute Information (in order):\n        - CRIM     per capita crime rate by town\n        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n        - INDUS    proportion of non-retail business acres per town\n        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n        - NOX      nitric oxides concentration (parts per 10 million)\n        - RM       average number of rooms per dwelling\n        - AGE      proportion of owner-occupied units built prior to 1940\n        - DIS      weighted distances to five Boston employment centres\n        - RAD      index of accessibility to radial highways\n        - TAX      full-value property-tax rate per $10,000\n        - PTRATIO  pupil-teacher ratio by town\n        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n        - LSTAT    % lower status of the population\n        - MEDV     Median value of owner-occupied homes in $1000's\n\n    :Missing Attribute Values: None\n\n    :Creator: Harrison, D. and Rubinfeld, D.L.\n\nThis is a copy of UCI ML housing dataset.\nhttps:\/\/archive.ics.uci.edu\/ml\/machine-learning-databases\/housing\/\n...<\/pre>\n\n\n\n
    DESCR \uc678\uc5d0\ub3c4 \ud574\ub2f9 boston \ub370\uc774\ud130\uc14b\uc5d0\ub294 \uba87\uac00\uc9c0\uc758 key \uac12\uc774 \ub354 \uc788\uc2b5\ub2c8\ub2e4. \ud574\ub2f9 \ud0a4\uac12\uc744 \ubaa8\ub450 \ubcf4\uae30 \uc704\ud574\uc11c\ub294 boston.key() \uba85\ub839\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n\n\n\n
    boston.keys()\n# dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])<\/pre>\n\n\n\n
    • data : \ub370\uc774\ud130\uc14b\uc758 \uc815\ubcf4\uac00 numpy.ndarray \ud615\ud0dc\ub85c \uc800\uc7a5<\/li>
    • target : \ub808\uc774\ube14 \uc815\ubcf4<\/li>
    • feature_names : \ub370\uc774\ud130\uc14b\uc758 \uceec\ub7fc\uc815\ubcf4<\/li>
    • DESCR : \ub370\uc774\ud130\uc14b \uc124\uba85<\/li>
    • filename : \ud574\ub2f9 \ub370\uc774\ud130\uc14b\uc758 \uc800\uc7a5 \uc704\uce58 \uc815\ubcf4 \ud45c\uc2dc<\/li><\/ul>\n\n\n\n
      \uc774\ub807\uac8c \uc785\ub825\ub41c \ub370\uc774\ud130\ub294 \uceec\ub7fc \uc815\ubcf4\uc640 \ubd84\ub9ac\ub418\uc5b4 \uc788\uae30 \ub54c\ubb38\uc5d0 \ubcf4\uae30\uc5d0 \uc880 \ubd88\ud3b8\ud558\uace0 \uc5b4\ub824\uc6b8 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7f4 \ub54c\uc5d0 pandas \ud328\ud0a4\uc9c0\ub97c \ud1b5\ud574 \ub370\uc774\ud130\ud504\ub808\uc784\uc744 \ub9cc\ub4e4\uba74 \ud0d0\uc0c9\uc801\ub370\uc774\ud130\ubd84\uc11d\uc5d0 \uc880 \ub354 \uc720\ub9ac\ud569\ub2c8\ub2e4.<\/p>\n\n\n\n
      import pandas as pd\ndf = pd.DataFrame(data=boston.data, columns = boston.feature_names)\ndf['TARGET'] = boston.target\ndf.head()<\/pre>\n\n\n\n
      \"\"<\/figure>\n\n\n\n
      pandas\uc758 \ub370\uc774\ud130\ud504\ub808\uc784\uc73c\ub85c \ud574\ub2f9 \ub370\uc774\ud130\ub97c \ubcc0\uacbd\ud574\ubcf4\uba74 \uc704\uc640 \uac19\uc740 \ud615\ud0dc\ub85c \ubcf4\uc5ec\uc9c0\uae30 \ub54c\ubb38\uc5d0 \ub370\uc774\ud130\ub97c \ubcf4\uae30\uac00 \uc880 \ub354 \ud3b8\ub9ac\ud569\ub2c8\ub2e4. \uc880 \ub354 \uc790\uc138\ud55c \uc815\ubcf4\ub97c \ubcf4\uc2dc\uace0 \uc2f6\uc73c\uc2dc\uba74 df.info(), df.describe()\uc640 \uac19\uc740 \ud568\uc218\ub97c \uc0ac\uc6a9\ud574\ubcf4\uc2dc\uba74 \uac01 \uceec\ub7fc\uc758 \ub370\uc774\ud130 \ud0c0\uc785\uacfc \uae30\ubcf8\uc801\uc778 \ud1b5\uacc4\uc815\ubcf4\ub97c \ud655\uc778 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ucd94\uac00\ub85c matplotlib\uacfc \uac19\uc740 \uc2dc\uac01\ud654 \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uba74 \ub370\uc774\ud130\ub97c \ub0b4\uc6a9\uc744 \ub354 \uc27d\uac8c \ud30c\uc545\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n\n\n\n
      import matplotlib.pyplot as plt\nplt.figure(figsize=(12,5))\nplt.scatter(scaled_df['AGE'],scaled_df['DIS'])<\/pre>\n\n\n\n
      \"\"<\/figure><\/div>\n\n\n\n
      corr = scaled_df.corr()\ncorr.style.background_gradient(cmap='coolwarm')<\/pre>\n\n\n\n
      \"\"<\/figure><\/div>\n\n\n\n
      \uc2dc\uac01\ud654\ub85c \ub370\uc774\ud130\uc14b\uc758 \ub0b4\uc6a9\uc744 \uc0b4\ud3b4\ubcf8 \ud6c4\uc5d0 \ud559\uc2b5\uc744 \uc704\ud574\uc11c \ub370\uc774\ud130\ub97c \ub098\ub220\uc904 \ud544\uc694\uac00 \uc788\uc744 \uacbd\uc6b0\uc5d0 sklearn\uc740 \ud6cc\ub96d\ud55c \uc720\ud2f8 \ud568\uc218\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc544\ub798\uc640 \uac19\uc774 model_selection \ud328\ud0a4\uc9c0\uc5d0\uc11c train_test_split\uc744 \uc0ac\uc6a9\ud574\uc11c \ud559\uc2b5\uc6a9, \ud6c8\ub828\uc6a9 \ub370\uc774\ud130\uc14b\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n\n\n\n
      from sklearn.model_selection import train_test_split\nx_train, x_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.2, shuffle=False, random_state=701)\nprint(x_train.shape) #404,13\nprint(x_test.shape) #102,13<\/pre>\n\n\n\n
      Load_Digit \ub370\uc774\ud130\uc14b<\/span><\/h4>\n\n\n\n
      boston \ub370\uc774\ud130\uc14b\uc740 \uc5f0\uc18d\ub41c \uac12\uc744 \uac00\uc9c0\ub294 \ud68c\uadc0\ubd84\uc11d\uc5d0\uc11c \ub9ce\uc774 \uc0ac\uc6a9\ud558\ub294 \uc608\uc81c\uc785\ub2c8\ub2e4. \uc774\ubc88\uc5d0\ub294 \ubd84\ub958 \ubb38\uc81c\uc5d0 \uc0ac\uc6a9\ud558\ub294 \ub370\uc774\ud130\uc14b\uc778 \uc22b\uc790(digit) \ub370\uc774\ud130\uc14b\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \ub370\uc774\ud130\uc758 shape\uc740 (1797, 64) \uc785\ub2c8\ub2e4.  \uc774\uac83\uc740 8 x 8 \uc774\ubbf8\uc9c0\ub97c  1\ucc28\uc6d0(64)\uc73c\ub85c \uc0dd\uc131\ud55c \uac12\uc778\ub370 \uc774\ub7ec\ud55c \uc774\ubbf8\uc9c0\uac00 1,797\uac1c\uac00 \uc788\ub2e4\ub294 \uc758\ubbf8\uc785\ub2c8\ub2e4.<\/p>\n\n\n\n
      from sklearn.datasets import load_digits\ndigits = load_digits()\ndigits.data[0]\narray([ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.,  0.,  0., 13., 15., 10.,\n       15.,  5.,  0.,  0.,  3., 15.,  2.,  0., 11.,  8.,  0.,  0.,  4.,\n       12.,  0.,  0.,  8.,  8.,  0.,  0.,  5.,  8.,  0.,  0.,  9.,  8.,\n        0.,  0.,  4., 11.,  0.,  1., 12.,  7.,  0.,  0.,  2., 14.,  5.,\n       10., 12.,  0.,  0.,  0.,  0.,  6., 13., 10.,  0.,  0.,  0.])<\/pre>\n\n\n\n
      plt.imshow(digits.data[0].reshape(8,8))<\/pre>\n\n\n\n
      \"\"<\/figure><\/div>\n\n\n\n
      \ud574\ub2f9 \ubc30\uc5f4\uc758 \uccab\ubc88\uc9f8 \uac12\uc744 \uac00\uc9c0\uace0 8×8\ubc30\uc5f4\ub85c \ub9cc\ub4e0 \ud6c4\uc5d0 \uc774\uac83\uc744 \uc774\ubbf8\uc9c0\ub85c \ud45c\uc2dc\ud574\ubcf4\uba74 \uc704\uc640 \uac19\uc740 \uc774\ubbf8\uc9c0 “0” \uac12\uc774 \ub098\uc635\ub2c8\ub2e4. \uc989 \uc774\ub7f0 \ub370\uc774\ud130\uac00 1,797\uac1c\uac00 \uc788\ub2e4\ub294 \uc758\ubbf8\uc785\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ub370\uc774\ud130\ub97c \ud6c8\ub828\uc2dc\ud0a4\uba74 \uc22b\uc790\uac00 \uc785\ub825\ub420 \ub54c \uc774\uac83\uc774 \uc5b4\ub5a4 \uc22b\uc790\uc778\uc9c0 \uc608\uce21\ud560 \uc218 \uc788\uac8c \ub429\ub2c8\ub2e4.<\/p>\n\n\n\n
      from sklearn.decomposition import PCA\npca = PCA(n_components=2) # \uc2dc\uac01\ud654\ub97c \uc704\ud574 2\ucc28\uc6d0\uc73c\ub85c \uc131\ubd84\uc744 \ucd94\ucd9c\ndim2data = pca.fit_transform(digits.data)\nplt.figure(figsize=(6,6))\nplt.scatter(dim2digit[:,0], dim2digit[:, 1], c=digits.target, linewidth=1)\nplt.show()<\/pre>\n\n\n\n
      \"\"<\/figure><\/div>\n","protected":false},"excerpt":{"rendered":"
      Load_Boston \ub370\uc774\ud130\uc14b sklearn\uc5d0\uc11c\ub294 \uac04\ub2e8\ud55c \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ubd84\uc11d\uacfc \ud14c\uc2a4\ud2b8\ub97c \uc704\ud574\uc11c \uc791\uc740 \uaddc\ubaa8\uc758 \ub370\uc774\ud130\uc14b(small toy datasets)\uc744 datasets\uc774\ub77c\ub294 \ud328\ud0a4\uc9c0\uc5d0 \ub2f4\uc544\ub450\uc5c8\uc2b5\ub2c8\ub2e4. \ud574\ub2f9 \ub370\uc774\ud130\uc14b\ub4e4\uc740 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc815\ubcf4\uc640 \ud2b9\uc9d5\ub4e4\uc774 \ud3ec\ud568\ub418\uc5b4 \uc788\uc5b4 \ub370\uc774\ud130\uc14b\uc744 \uc774\uc6a9\ud558\ub294 \uc0ac\uc6a9\uc790\ub85c \ud558\uc5ec\uae08 \ud574\ub2f9 \ub370\uc774\ud130\uc14b\uc774 \uc5b4\ub5a4 \uc758\ubbf8\uc778\uac00\ub97c \uc27d\uac8c \uc774\ud574\ud560 \uc218 \uc788\ub3c4\ub85d \ud574\uc8fc\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uacf5\uc2dd \ud648\ud398\uc774\uc9c0\uc5d0 \ub4e4\uc5b4\uac00\ubcf4\uc2dc\uba74 \uc81c\uacf5\ud558\ub294 \ub370\uc774\ud130\uc758 \ub0b4\uc6a9\ub4e4\uc744 \ud655\uc778 \ud558\uc2e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uac04\ub7b5\ud55c \uc815\ubcf4\ub294 \uc544\ub798\uc640 \uac19\uc2b5\ub2c8\ub2e4. load_boston : … <\/p>\n
      \ub354 \ubcf4\uae30 “sklearn.datasets”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[18],"tags":[58,54,48,55],"_links":{"self":[{"href":"http:\/\/blog.cedartrees.co.kr\/index.php\/wp-json\/wp\/v2\/posts\/580"}],"collection":[{"href":"http:\/\/blog.cedartrees.co.kr\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/blog.cedartrees.co.kr\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/blog.cedartrees.co.kr\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/blog.cedartrees.co.kr\/index.php\/wp-json\/wp\/v2\/comments?post=580"}],"version-history":[{"count":12,"href":"http:\/\/blog.cedartrees.co.kr\/index.php\/wp-json\/wp\/v2\/posts\/580\/revisions"}],"predecessor-version":[{"id":605,"href":"http:\/\/blog.cedartrees.co.kr\/index.php\/wp-json\/wp\/v2\/posts\/580\/revisions\/605"}],"wp:attachment":[{"href":"http:\/\/blog.cedartrees.co.kr\/index.php\/wp-json\/wp\/v2\/media?parent=580"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/blog.cedartrees.co.kr\/index.php\/wp-json\/wp\/v2\/categories?post=580"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/blog.cedartrees.co.kr\/index.php\/wp-json\/wp\/v2\/tags?post=580"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}