@@ -2,33 +2,52 @@ Sebastian Raschka, 2015
22
33# Python Machine Learning - Supplementary Datasets
44
5- ### iris
6-
7- - used in chapters 1, 2, and 3
8- - source: [ https://archive.ics.uci.edu/ml/datasets/Iris ] ( https://archive.ics.uci.edu/ml/datasets/Iris )
9-
10- ### wine
11-
12- - used in chapters 4 and 5
13- - source: [ https://archive.ics.uci.edu/ml/datasets/Wine ] ( https://archive.ics.uci.edu/ml/datasets/Wine )
14-
15- ### wdbc
16-
17- - used in chapter 6
18- - source: [ https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic) ] ( https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic) )
19-
20- ### movie
21-
22- - used in chapters 8 and 9
23- - movie dataset converted into a 2-column CSV format: The first column (` review ` ) contains the text, and the second column (` sentiment ` ) denotes the polarity, where 0=negative and 1=positive. The first 25,000 are the training samples and the remaining 25,000 rows are the test samples from the "Large Movie Review Dataset v1.0," respectively.
24- - source: [ http://ai.stanford.edu/~amaas/data/sentiment/ ] ( http://ai.stanford.edu/~amaas/data/sentiment/ )
25-
26- ### housing
27-
28- - used in chapter 10
29- - source: [ https://archive.ics.uci.edu/ml/datasets/Housing ] ( https://archive.ics.uci.edu/ml/datasets/Housing )
30-
31- ### mnist
32-
33- - used in chapter 12, 13
34- - source: [ http://yann.lecun.com/exdb/mnist/ ]
5+ ## Wine Dataset
6+
7+ - Used in chapters 4 and 5
8+
9+ The Wine dataset for classification.
10+
11+ | | |
12+ | ----------------------------| ----------------|
13+ | Samples | 178 |
14+ | Features | 13 |
15+ | Classes | 3 |
16+ | Data Set Characteristics: | Multivariate |
17+ | Attribute Characteristics: | Integer, Real |
18+ | Associated Tasks: | Classification |
19+ | Missing Values | None |
20+
21+ | column| attribute |
22+ | -----| ------------------------------|
23+ | 1) | Class Label |
24+ | 2) | Alcohol |
25+ | 3) | Malic acid |
26+ | 4) | Ash |
27+ | 5) | Alcalinity of ash |
28+ | 6) | Magnesium |
29+ | 7) | Total phenols |
30+ | 8) | Flavanoids |
31+ | 9) | Nonflavanoid phenols |
32+ | 10) | Proanthocyanins |
33+ | 11) | intensity |
34+ | 12) | Hue |
35+ | 13) | OD280/OD315 of diluted wines |
36+ | 14) | Proline |
37+
38+
39+ | class | samples |
40+ | -------| ----|
41+ | 0 | 59 |
42+ | 1 | 71 |
43+ | 2 | 48 |
44+
45+
46+ ### References
47+
48+ - Forina, M. et al, PARVUS -
49+ An Extendible Package for Data Exploration, Classification and Correlation.
50+ Institute of Pharmaceutical and Food Analysis and Technologies, Via Brigata Salerno,
51+ 16147 Genoa, Italy.
52+ - Source: [ https://archive.ics.uci.edu/ml/datasets/Wine ] ( https://archive.ics.uci.edu/ml/datasets/Wine )
53+ - Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.
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