# 2.6. Datasets¶

## 2.6.2. Iris Flower Dataset¶

The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by the British statistician and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis.

The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres. Based on the combination of these four features, Fisher developed a linear discriminant model to distinguish the species from each other.

Figure 2.9. Scatterplot of the Iris data set

Based on Fisher's linear discriminant model, this data set became a typical test case for many statistical classification techniques in machine learning such as support vector machines.

Figure 2.10. Unsatisfactory k-means clustering result (the data set does not cluster into the known classes) and actual species visualized using ELKI

from sklearn.datasets import load_iris

iris.feature_names
# ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']

iris.target_names
# ['setosa' 'versicolor' 'virginica']

iris.data[0]
# [5.1  3.5  1.4  0.2]

print(iris.target[0]
# 0


## 2.6.3. Pima Indians Diabetes problem¶

Dataset:

This problem is comprised of 768 observations of medical details for Pima indians patents. The records describe instantaneous measurements taken from the patient such as their age, the number of times pregnant and blood workup. All patients are women aged 21 or older. All attributes are numeric, and their units vary from attribute to attribute.

• Number of times pregnant

• Plasma glucose concentration a 2 hours in an oral glucose tolerance test

• Diastolic blood pressure (mm Hg)

• Triceps skin fold thickness (mm)

• 2-Hour serum insulin (mu U/ml)

• Body mass index (weight in kg/(height in m)^2)

• Diabetes pedigree function

• Age (years)

• Class variable (0 or 1)

Each record has a class value that indicates whether the patient suffered an onset of diabetes within 5 years of when the measurements were taken (1) or not (0).

This is a standard dataset that has been studied a lot in machine learning literature. A good prediction accuracy is 70%-76%.

## 2.6.4. Wisconsin Breast Cancer Database¶

URL

https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/

Struktura
 # Attribute Domain Sample code number id number Clump Thickness 1 - 10 Uniformity of Cell Size 1 - 10 Uniformity of Cell Shape 1 - 10 Marginal Adhesion 1 - 10 Single Epithelial Cell Size 1 - 10 Bare Nuclei 1 - 10 Bland Chromatin 1 - 10 Normal Nucleoli 1 - 10 Mitoses 1 - 10 Class: (2 for benign, 4 for malignant)

## 2.6.5. Quandl¶

Quandl is a platform for financial, economic, and alternative data that serves investment professionals. Quandl sources data from over 500 publishers. All Quandl data are accessible via an API. API access is possible through packages for multiple programming languages including R, Python, Matlab, Maple (software) and Stata.

An Excel add-in allows access to data, including stock price information.

Package for quandl API access https://www.quandl.com/topics

## 2.6.7. SCI-Kit Datasets¶

The sklearn.datasets package embeds some small toy datasets. To evaluate the impact of the scale of the dataset (n_samples and n_features) while controlling the statistical properties of the data (typically the correlation and informativeness of the features), it is also possible to generate synthetic data.

This package also features helpers to fetch larger datasets commonly used by the machine learning community to benchmark algorithm on data that comes from the 'real world'.

'clear_data_home',
'dump_svmlight_file',
'fetch_20newsgroups',
'fetch_20newsgroups_vectorized',
'fetch_lfw_pairs',
'fetch_lfw_people',
'fetch_mldata',
'fetch_olivetti_faces',
'fetch_species_distributions',
'fetch_california_housing',
'fetch_covtype',
'fetch_rcv1',
'fetch_kddcup99',
'get_data_home',

'load_boston',

'make_biclusters',
'make_blobs',
'make_circles',
'make_classification',
'make_checkerboard',
'make_friedman1',
'make_friedman2',
'make_friedman3',
'make_gaussian_quantiles',
'make_hastie_10_2',
'make_low_rank_matrix',
'make_moons',
'make_multilabel_classification',
'make_regression',
'make_s_curve',
'make_sparse_coded_signal',
'make_sparse_spd_matrix',
'make_sparse_uncorrelated',
'make_spd_matrix',
'make_swiss_roll',

'mldata_filename'


## 2.6.8. Eurostat datasets¶

http://ec.europa.eu/eurostat/data/database

## 2.6.9. ML Data¶

mldata.org is a public repository for machine learning data, supported by the PASCAL network.

The sklearn.datasets package is able to directly download data sets from the repository using the function sklearn.datasets.fetch_mldata.

For example, to download the MNIST digit recognition database:

>>> from sklearn.datasets import fetch_mldata
>>> mnist = fetch_mldata('MNIST original', data_home=custom_data_home)


## 2.6.10. PASCAL¶

PASCAL is a Network of Excellence funded by the European Union. It has established a distributed institute that brings together researchers and students across Europe, and is now reaching out to countries all over the world.

PASCAL is developing the expertise and scientific results that will help create new technologies such as intelligent interfaces and adaptive cognitive systems. To achieve this, it supports and encourages collaboration between experts in Machine Learning, Statistics and Optimization. It also promotes the use of machine learning in many relevant application domains such as:

• Machine Vision

• Speech

• Haptics

• Brain-Computer Interface

• User-modeling for computer human interaction

• Multimodal integration

• Natural Language Processing

• Information Retrieval

• Textual Information Access