sklearn數據集

1、數據集劃分

2、sklearn數據集接口介紹

3、 sklearn分類數據集

4、 sklearn回歸數據集

數據集劃分

機器學習一般的數據集會劃分為兩個部分：

訓練數據：用于訓練，構建模型

測試數據：在模型檢驗時使用，用于評估模型是否有效

sklearn數據集接口介紹

sklearn數據集劃分API：sklearn.model_selection.train_test_split

scikit-learn數據集API介紹

sklearn.datasets加載獲取流行數據集datasets.load_*()獲取小規模數據集，數據包含在datasets里datasets.fetch_*(data_home=None)獲取大規模數據集，需要從網絡上下載，函數的第一個參數是data_home，表示數據集下載的目錄,默認是 ~/scikit_learn_data/

sklearn分類數據集

sklearn.datasets.load_iris() 加載并返回鳶尾花數據集

獲取數據集返回的類型

load*和fetch*返回的數據類型datasets.base.Bunch(字典格式)data：特征數據數組，是 [n_samples * n_features] 的二維 numpy.ndarray 數組target：標簽數組，是 n_samples 的一維 numpy.ndarray 數組DESCR：數據描述feature_names：特征名,新聞數據，手寫數字、回歸數據集沒有target_names：標簽名,回歸數據集沒有 from sklearn.datasets import load_irisli=load_iris()print("獲取特征值") print(li.data)print("獲取目標值") print(li.target)print(li.DESCR) 獲取特征值 [[5.1 3.5 1.4 0.2][4.9 3. 1.4 0.2][4.7 3.2 1.3 0.2][4.6 3.1 1.5 0.2][5. 3.6 1.4 0.2][5.4 3.9 1.7 0.4][4.6 3.4 1.4 0.3][5. 3.4 1.5 0.2][4.4 2.9 1.4 0.2][4.9 3.1 1.5 0.1][5.4 3.7 1.5 0.2][4.8 3.4 1.6 0.2][4.8 3. 1.4 0.1][4.3 3. 1.1 0.1][5.8 4. 1.2 0.2][5.7 4.4 1.5 0.4][5.4 3.9 1.3 0.4][5.1 3.5 1.4 0.3][5.7 3.8 1.7 0.3][5.1 3.8 1.5 0.3][5.4 3.4 1.7 0.2][5.1 3.7 1.5 0.4][4.6 3.6 1. 0.2][5.1 3.3 1.7 0.5][4.8 3.4 1.9 0.2][5. 3. 1.6 0.2][5. 3.4 1.6 0.4][5.2 3.5 1.5 0.2][5.2 3.4 1.4 0.2][4.7 3.2 1.6 0.2][4.8 3.1 1.6 0.2][5.4 3.4 1.5 0.4][5.2 4.1 1.5 0.1][5.5 4.2 1.4 0.2][4.9 3.1 1.5 0.2][5. 3.2 1.2 0.2][5.5 3.5 1.3 0.2][4.9 3.6 1.4 0.1][4.4 3. 1.3 0.2][5.1 3.4 1.5 0.2][5. 3.5 1.3 0.3][4.5 2.3 1.3 0.3][4.4 3.2 1.3 0.2][5. 3.5 1.6 0.6][5.1 3.8 1.9 0.4][4.8 3. 1.4 0.3][5.1 3.8 1.6 0.2][4.6 3.2 1.4 0.2][5.3 3.7 1.5 0.2][5. 3.3 1.4 0.2][7. 3.2 4.7 1.4][6.4 3.2 4.5 1.5][6.9 3.1 4.9 1.5][5.5 2.3 4. 1.3][6.5 2.8 4.6 1.5][5.7 2.8 4.5 1.3][6.3 3.3 4.7 1.6][4.9 2.4 3.3 1. ][6.6 2.9 4.6 1.3][5.2 2.7 3.9 1.4][5. 2. 3.5 1. ][5.9 3. 4.2 1.5][6. 2.2 4. 1. ][6.1 2.9 4.7 1.4][5.6 2.9 3.6 1.3][6.7 3.1 4.4 1.4][5.6 3. 4.5 1.5][5.8 2.7 4.1 1. ][6.2 2.2 4.5 1.5][5.6 2.5 3.9 1.1][5.9 3.2 4.8 1.8][6.1 2.8 4. 1.3][6.3 2.5 4.9 1.5][6.1 2.8 4.7 1.2][6.4 2.9 4.3 1.3][6.6 3. 4.4 1.4][6.8 2.8 4.8 1.4][6.7 3. 5. 1.7][6. 2.9 4.5 1.5][5.7 2.6 3.5 1. ][5.5 2.4 3.8 1.1][5.5 2.4 3.7 1. ][5.8 2.7 3.9 1.2][6. 2.7 5.1 1.6][5.4 3. 4.5 1.5][6. 3.4 4.5 1.6][6.7 3.1 4.7 1.5][6.3 2.3 4.4 1.3][5.6 3. 4.1 1.3][5.5 2.5 4. 1.3][5.5 2.6 4.4 1.2][6.1 3. 4.6 1.4][5.8 2.6 4. 1.2][5. 2.3 3.3 1. ][5.6 2.7 4.2 1.3][5.7 3. 4.2 1.2][5.7 2.9 4.2 1.3][6.2 2.9 4.3 1.3][5.1 2.5 3. 1.1][5.7 2.8 4.1 1.3][6.3 3.3 6. 2.5][5.8 2.7 5.1 1.9][7.1 3. 5.9 2.1][6.3 2.9 5.6 1.8][6.5 3. 5.8 2.2][7.6 3. 6.6 2.1][4.9 2.5 4.5 1.7][7.3 2.9 6.3 1.8][6.7 2.5 5.8 1.8][7.2 3.6 6.1 2.5][6.5 3.2 5.1 2. ][6.4 2.7 5.3 1.9][6.8 3. 5.5 2.1][5.7 2.5 5. 2. ][5.8 2.8 5.1 2.4][6.4 3.2 5.3 2.3][6.5 3. 5.5 1.8][7.7 3.8 6.7 2.2][7.7 2.6 6.9 2.3][6. 2.2 5. 1.5][6.9 3.2 5.7 2.3][5.6 2.8 4.9 2. ][7.7 2.8 6.7 2. ][6.3 2.7 4.9 1.8][6.7 3.3 5.7 2.1][7.2 3.2 6. 1.8][6.2 2.8 4.8 1.8][6.1 3. 4.9 1.8][6.4 2.8 5.6 2.1][7.2 3. 5.8 1.6][7.4 2.8 6.1 1.9][7.9 3.8 6.4 2. ][6.4 2.8 5.6 2.2][6.3 2.8 5.1 1.5][6.1 2.6 5.6 1.4][7.7 3. 6.1 2.3][6.3 3.4 5.6 2.4][6.4 3.1 5.5 1.8][6. 3. 4.8 1.8][6.9 3.1 5.4 2.1][6.7 3.1 5.6 2.4][6.9 3.1 5.1 2.3][5.8 2.7 5.1 1.9][6.8 3.2 5.9 2.3][6.7 3.3 5.7 2.5][6.7 3. 5.2 2.3][6.3 2.5 5. 1.9][6.5 3. 5.2 2. ][6.2 3.4 5.4 2.3][5.9 3. 5.1 1.8]] 獲取目標值 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 22 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 22 2] .. _iris_dataset:Iris plants dataset --------------------**Data Set Characteristics:**:Number of Instances: 150 (50 in each of three classes):Number of Attributes: 4 numeric, predictive attributes and the class:Attribute Information:- sepal length in cm- sepal width in cm- petal length in cm- petal width in cm- class:- Iris-Setosa- Iris-Versicolour- Iris-Virginica:Summary Statistics:============== ==== ==== ======= ===== ====================Min Max Mean SD Class Correlation============== ==== ==== ======= ===== ====================sepal length: 4.3 7.9 5.84 0.83 0.7826sepal width: 2.0 4.4 3.05 0.43 -0.4194petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)============== ==== ==== ======= ===== ====================:Missing Attribute Values: None:Class Distribution: 33.3% for each of 3 classes.:Creator: R.A. Fisher:Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov):Date: July, 1988The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken from Fisher's paper. Note that it's the same as in R, but not as in the UCI Machine Learning Repository, which has two wrong data points.This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other... topic:: References- Fisher, R.A. "The use of multiple measurements in taxonomic problems"Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions toMathematical Statistics" (John Wiley, NY, 1950).- Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.(Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.- Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New SystemStructure and Classification Rule for Recognition in Partially ExposedEnvironments". IEEE Transactions on Pattern Analysis and MachineIntelligence, Vol. PAMI-2, No. 1, 67-71.- Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactionson Information Theory, May 1972, 431-433.- See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS IIconceptual clustering system finds 3 classes in the data.- Many, many more ...

數據集進行分割

sklearn.model_selection.train_test_split(*arrays, **options)

x 數據集的特征值 y 數據集的標簽值 test_size 測試集的大小，一般為float random_state 隨機數種子,不同的種子會造成不同的隨機 采樣結果。相同的種子采樣結果相同。 return 訓練集特征值，測試集特征值，訓練標簽，測試標簽(默認隨機取) from sklearn.datasets import load_iris from sklearn.model_selection import train_test_splitli=load_iris()#注意返回值，既包含訓練集也包含測試集 x_train,x_test,y_train,y_test=train_test_split(li.data,li.target,test_size=0.25)print("訓練集特征值和目標值",x_train,y_train) print("測試集特征值和目標值",x_test,y_test) 訓練集特征值和目標值 [[5. 2.3 3.3 1. ][6.7 3.1 5.6 2.4][6. 2.7 5.1 1.6][6.3 3.3 6. 2.5][6. 2.2 4. 1. ][4.9 3.1 1.5 0.1][7.7 2.6 6.9 2.3][4.3 3. 1.1 0.1][5.8 2.7 5.1 1.9][5.2 3.5 1.5 0.2][5.2 3.4 1.4 0.2][5. 3.5 1.3 0.3][5.1 3.5 1.4 0.3][5.5 2.5 4. 1.3][5.1 3.3 1.7 0.5][5.1 3.8 1.9 0.4][6. 2.9 4.5 1.5][5.8 2.7 3.9 1.2][5.4 3.9 1.7 0.4][5.7 2.9 4.2 1.3][6.3 2.5 4.9 1.5][6.7 3.1 4.7 1.5][6.4 2.7 5.3 1.9][5.1 3.4 1.5 0.2][4.9 2.4 3.3 1. ][6.3 2.5 5. 1.9][5.8 4. 1.2 0.2][5.4 3.7 1.5 0.2][6.2 2.9 4.3 1.3][6.1 2.9 4.7 1.4][6.9 3.2 5.7 2.3][5. 3.4 1.6 0.4][6.4 3.1 5.5 1.8][7. 3.2 4.7 1.4][4.6 3.6 1. 0.2][5.9 3. 4.2 1.5][5.6 3. 4.5 1.5][7.7 2.8 6.7 2. ][5.8 2.6 4. 1.2][4.4 3. 1.3 0.2][4.6 3.4 1.4 0.3][5.1 3.8 1.5 0.3][6.6 3. 4.4 1.4][5.7 4.4 1.5 0.4][6.4 2.8 5.6 2.1][6.9 3.1 5.1 2.3][5.6 2.7 4.2 1.3][7.3 2.9 6.3 1.8][4.7 3.2 1.6 0.2][4.8 3.4 1.6 0.2][5. 3.2 1.2 0.2][5.6 3. 4.1 1.3][5.5 2.4 3.8 1.1][4.8 3. 1.4 0.1][5.1 3.7 1.5 0.4][5. 3.6 1.4 0.2][7.7 3.8 6.7 2.2][4.8 3.1 1.6 0.2][5.9 3. 5.1 1.8][5.7 2.6 3.5 1. ][6.4 3.2 5.3 2.3][5.8 2.8 5.1 2.4][4.4 3.2 1.3 0.2][5. 3.3 1.4 0.2][6.5 3.2 5.1 2. ][5.1 3.5 1.4 0.2][6.5 3. 5.8 2.2][6.1 2.6 5.6 1.4][7.2 3.6 6.1 2.5][5.5 2.4 3.7 1. ][5.8 2.7 5.1 1.9][7.7 3. 6.1 2.3][5. 3. 1.6 0.2][6.9 3.1 5.4 2.1][7.1 3. 5.9 2.1][5.4 3.4 1.7 0.2][6.1 2.8 4. 1.3][5.3 3.7 1.5 0.2][7.2 3. 5.8 1.6][6.2 2.8 4.8 1.8][5.4 3.4 1.5 0.4][7.4 2.8 6.1 1.9][6.7 3.3 5.7 2.1][5.7 3.8 1.7 0.3][5.6 2.5 3.9 1.1][4.8 3.4 1.9 0.2][6.7 3. 5. 1.7][6.5 2.8 4.6 1.5][4.9 3. 1.4 0.2][4.5 2.3 1.3 0.3][5.5 2.6 4.4 1.2][6.1 3. 4.6 1.4][6.4 2.8 5.6 2.2][4.9 3.1 1.5 0.2][6.3 3.4 5.6 2.4][6. 3. 4.8 1.8][5.2 4.1 1.5 0.1][5.7 2.8 4.1 1.3][7.9 3.8 6.4 2. ][4.7 3.2 1.3 0.2][6.3 2.8 5.1 1.5][4.8 3. 1.4 0.3][5.7 2.5 5. 2. ][5.7 2.8 4.5 1.3][6.4 2.9 4.3 1.3][4.9 3.6 1.4 0.1][5. 3.5 1.6 0.6][6.8 2.8 4.8 1.4][5.5 4.2 1.4 0.2][5.8 2.7 4.1 1. ][5.7 3. 4.2 1.2][6.3 2.9 5.6 1.8]] [1 2 1 2 1 0 2 0 2 0 0 0 0 1 0 0 1 1 0 1 1 1 2 0 1 2 0 0 1 1 2 0 2 1 0 1 12 1 0 0 0 1 0 2 2 1 2 0 0 0 1 1 0 0 0 2 0 2 1 2 2 0 0 2 0 2 2 2 1 2 2 0 22 0 1 0 2 2 0 2 2 0 1 0 1 1 0 0 1 1 2 0 2 2 0 1 2 0 2 0 2 1 1 0 0 1 0 1 12] 測試集特征值和目標值 [[6.2 2.2 4.5 1.5][6.7 3. 5.2 2.3][6.9 3.1 4.9 1.5][6.4 3.2 4.5 1.5][4.6 3.2 1.4 0.2][4.9 2.5 4.5 1.7][5.6 2.9 3.6 1.3][6.3 2.7 4.9 1.8][7.6 3. 6.6 2.1][4.4 2.9 1.4 0.2][5. 2. 3.5 1. ][6.2 3.4 5.4 2.3][6.5 3. 5.2 2. ][6.1 3. 4.9 1.8][6.6 2.9 4.6 1.3][6.3 3.3 4.7 1.6][6.3 2.3 4.4 1.3][6.8 3.2 5.9 2.3][6.8 3. 5.5 2.1][5.1 2.5 3. 1.1][4.6 3.1 1.5 0.2][5.5 2.3 4. 1.3][6.7 3.1 4.4 1.4][6.1 2.8 4.7 1.2][6. 3.4 4.5 1.6][5.2 2.7 3.9 1.4][5.1 3.8 1.6 0.2][6.5 3. 5.5 1.8][5.5 3.5 1.3 0.2][5. 3.4 1.5 0.2][6.7 2.5 5.8 1.8][5.6 2.8 4.9 2. ][5.9 3.2 4.8 1.8][5.4 3. 4.5 1.5][5.4 3.9 1.3 0.4][7.2 3.2 6. 1.8][6. 2.2 5. 1.5][6.7 3.3 5.7 2.5]] [1 2 1 1 0 2 1 2 2 0 1 2 2 2 1 1 1 2 2 1 0 1 1 1 1 1 0 2 0 0 2 2 1 1 0 2 22]

用于分類的大數據集

sklearn.datasets.fetch_20newsgroups(data_home=None,subset=‘train’)subset: 'train'或者'test','all'，可選，選擇要加載的數據集.訓練集的“訓練”，測試集的“測試”，兩者的“全部”datasets.clear_data_home(data_home=None)清除目錄下的數據

sklearn回歸數據集

sklearn.datasets.load_boston() 加載并返回波士頓房價數據集

from sklearn.datasets import load_iris,load_bostonlb = load_boston()print("獲取特征值") print(lb.data) print("目標值") print(lb.target) print(lb.DESCR) 獲取特征值 [[6.3200e-03 1.8000e+01 2.3100e+00 ... 1.5300e+01 3.9690e+02 4.9800e+00][2.7310e-02 0.0000e+00 7.0700e+00 ... 1.7800e+01 3.9690e+02 9.1400e+00][2.7290e-02 0.0000e+00 7.0700e+00 ... 1.7800e+01 3.9283e+02 4.0300e+00]...[6.0760e-02 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9690e+02 5.6400e+00][1.0959e-01 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9345e+02 6.4800e+00][4.7410e-02 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9690e+02 7.8800e+00]] 目標值 [24. 21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 15. 18.9 21.7 20.418.2 19.9 23.1 17.5 20.2 18.2 13.6 19.6 15.2 14.5 15.6 13.9 16.6 14.818.4 21. 12.7 14.5 13.2 13.1 13.5 18.9 20. 21. 24.7 30.8 34.9 26.625.3 24.7 21.2 19.3 20. 16.6 14.4 19.4 19.7 20.5 25. 23.4 18.9 35.424.7 31.6 23.3 19.6 18.7 16. 22.2 25. 33. 23.5 19.4 22. 17.4 20.924.2 21.7 22.8 23.4 24.1 21.4 20. 20.8 21.2 20.3 28. 23.9 24.8 22.923.9 26.6 22.5 22.2 23.6 28.7 22.6 22. 22.9 25. 20.6 28.4 21.4 38.743.8 33.2 27.5 26.5 18.6 19.3 20.1 19.5 19.5 20.4 19.8 19.4 21.7 22.818.8 18.7 18.5 18.3 21.2 19.2 20.4 19.3 22. 20.3 20.5 17.3 18.8 21.415.7 16.2 18. 14.3 19.2 19.6 23. 18.4 15.6 18.1 17.4 17.1 13.3 17.814. 14.4 13.4 15.6 11.8 13.8 15.6 14.6 17.8 15.4 21.5 19.6 15.3 19.417. 15.6 13.1 41.3 24.3 23.3 27. 50. 50. 50. 22.7 25. 50. 23.823.8 22.3 17.4 19.1 23.1 23.6 22.6 29.4 23.2 24.6 29.9 37.2 39.8 36.237.9 32.5 26.4 29.6 50. 32. 29.8 34.9 37. 30.5 36.4 31.1 29.1 50.33.3 30.3 34.6 34.9 32.9 24.1 42.3 48.5 50. 22.6 24.4 22.5 24.4 20.21.7 19.3 22.4 28.1 23.7 25. 23.3 28.7 21.5 23. 26.7 21.7 27.5 30.144.8 50. 37.6 31.6 46.7 31.5 24.3 31.7 41.7 48.3 29. 24. 25.1 31.523.7 23.3 22. 20.1 22.2 23.7 17.6 18.5 24.3 20.5 24.5 26.2 24.4 24.829.6 42.8 21.9 20.9 44. 50. 36. 30.1 33.8 43.1 48.8 31. 36.5 22.830.7 50. 43.5 20.7 21.1 25.2 24.4 35.2 32.4 32. 33.2 33.1 29.1 35.145.4 35.4 46. 50. 32.2 22. 20.1 23.2 22.3 24.8 28.5 37.3 27.9 23.921.7 28.6 27.1 20.3 22.5 29. 24.8 22. 26.4 33.1 36.1 28.4 33.4 28.222.8 20.3 16.1 22.1 19.4 21.6 23.8 16.2 17.8 19.8 23.1 21. 23.8 23.120.4 18.5 25. 24.6 23. 22.2 19.3 22.6 19.8 17.1 19.4 22.2 20.7 21.119.5 18.5 20.6 19. 18.7 32.7 16.5 23.9 31.2 17.5 17.2 23.1 24.5 26.622.9 24.1 18.6 30.1 18.2 20.6 17.8 21.7 22.7 22.6 25. 19.9 20.8 16.821.9 27.5 21.9 23.1 50. 50. 50. 50. 50. 13.8 13.8 15. 13.9 13.313.1 10.2 10.4 10.9 11.3 12.3 8.8 7.2 10.5 7.4 10.2 11.5 15.1 23.29.7 13.8 12.7 13.1 12.5 8.5 5. 6.3 5.6 7.2 12.1 8.3 8.5 5.11.9 27.9 17.2 27.5 15. 17.2 17.9 16.3 7. 7.2 7.5 10.4 8.8 8.416.7 14.2 20.8 13.4 11.7 8.3 10.2 10.9 11. 9.5 14.5 14.1 16.1 14.311.7 13.4 9.6 8.7 8.4 12.8 10.5 17.1 18.4 15.4 10.8 11.8 14.9 12.614.1 13. 13.4 15.2 16.1 17.8 14.9 14.1 12.7 13.5 14.9 20. 16.4 17.719.5 20.2 21.4 19.9 19. 19.1 19.1 20.1 19.9 19.6 23.2 29.8 13.8 13.316.7 12. 14.6 21.4 23. 23.7 25. 21.8 20.6 21.2 19.1 20.6 15.2 7.8.1 13.6 20.1 21.8 24.5 23.1 19.7 18.3 21.2 17.5 16.8 22.4 20.6 23.922. 11.9] .. _boston_dataset:Boston house prices dataset ---------------------------**Data Set Characteristics:** :Number of Instances: 506 :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.:Attribute Information (in order):- CRIM per capita crime rate by town- ZN proportion of residential land zoned for lots over 25,000 sq.ft.- INDUS proportion of non-retail business acres per town- CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)- NOX nitric oxides concentration (parts per 10 million)- RM average number of rooms per dwelling- AGE proportion of owner-occupied units built prior to 1940- DIS weighted distances to five Boston employment centres- RAD index of accessibility to radial highways- TAX full-value property-tax rate per $10,000- PTRATIO pupil-teacher ratio by town- B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town- LSTAT % lower status of the population- MEDV Median value of owner-occupied homes in $1000's:Missing Attribute Values: None:Creator: Harrison, D. and Rubinfeld, D.L.This is a copy of UCI ML housing dataset. https://archive.ics.uci.edu/ml/machine-learning-databases/housing/This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics ...', Wiley, 1980. N.B. Various transformations are used in the table on pages 244-261 of the latter.The Boston house-price data has been used in many machine learning papers that address regression problems. .. topic:: References- Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.- Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.

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