Latter are NOT linearly separable from each other.įisher, R.A. One class is linearly separable from the other 2 the (See Duda & Hart, for example.) Theĭata set contains 3 classes of 50 instances each, where each class refers to a Fisher’s paper is a classic in the field and This is perhaps the best known database to be found in the Machine Learning Repository, which has two wrong data points. Note that it’s the same as in R, but not as in the UCI The famous Iris database, first used by Sir R.A. Morgan Kaufmann.ġ50 (50 in each of three classes) Number of Attributes :Ĥ numeric, predictive attributes and the class Attribute Information : In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Combining Instance-Based and Model-Based Learning. The Boston house-price data has been used in many machine learning papers that address regressionīelsley, Kuh & Welsch, ‘Regression diagnostics: Identifying Influential Data and Sources of Collinearity’, Wiley, 1980. Various transformations are used in the table on Used in Belsley, Kuh & Welsch, ‘Regression diagnostics The Boston house-price data of Harrison, D.
This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. This is a copy of UCI ML housing dataset. MEDV Median value of owner-occupied homes in $1000’s TAX full-value property-tax rate per $10,000ī 1000(Bk - 0.63)^2 where Bk is the proportion of black people by town RAD index of accessibility to radial highways NOX nitric oxides concentration (parts per 10 million)ĪGE proportion of owner-occupied units built prior to 1940ĭIS weighted distances to five Boston employment centres INDUS proportion of non-retail business acres per townĬHAS Charles River dummy variable (= 1 if tract bounds river 0 otherwise) ZN proportion of residential land zoned for lots over 25,000 sq.ft. Median Value (attribute 14) is usually the target. Boston house prices dataset ¶ġ3 numeric/categorical predictive. Small to be representative of real world machine learning tasks. Various algorithms implemented in scikit-learn. These datasets are useful to quickly illustrate the behavior of the Load and return the breast cancer wisconsin dataset (classification). Load and return the wine dataset (classification). Load and return the physical exercise Linnerud dataset. Load and return the digits dataset (classification). Load and return the diabetes dataset (regression).
Load and return the iris dataset (classification). They can be loaded using the following functions:ĭEPRECATED: load_boston is deprecated in 1.0 and will be removed in 1.2. Scikit-learn comes with a few small standard datasets that do not require toĭownload any file from some external website.
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