5.1. Skąd wziąć dane testowe?
5.1.1. 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.
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.
from sklearn.datasets import load_iris iris = 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 # [5.1 3.5 1.4 0.2] print(iris.target # 0
5.1.2. Pima Indians Diabetes problem
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
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%.
5.1.3. Wisconsin Breast Cancer Database
Sample code number
1 - 10
Uniformity of Cell Size
1 - 10
Uniformity of Cell Shape
1 - 10
1 - 10
Single Epithelial Cell Size
1 - 10
1 - 10
1 - 10
1 - 10
1 - 10
(2 for benign, 4 for malignant)
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5.1.5. SPAM Dataset
5.1.6. SCI-Kit Datasets
sklearn.datasets package embeds some small toy datasets. To evaluate the impact of the scale of the dataset (
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', 'load_diabetes', 'load_digits', 'load_files', 'load_iris', 'load_breast_cancer', 'load_linnerud', 'load_mlcomp', 'load_sample_image', 'load_sample_images', 'load_svmlight_file', 'load_svmlight_files', 'load_wine',
'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',
5.1.7. Eurostat datasets
5.1.8. ML Data
The sklearn.datasets package is able to directly download data sets from the repository using the function
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)
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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:
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Natural Language Processing
Textual Information Access
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