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env-llmeval/lib/python3.10/site-packages/sklearn/datasets/data/linnerud_physiological.csv

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+Weight Waist Pulse
+191 36 50
+189 37 52
+193 38 58
+162 35 62
+189 35 46
+182 36 56
+211 38 56
+167 34 60
+176 31 74
+154 33 56
+169 34 50
+166 33 52
+154 34 64
+247 46 50
+193 36 46
+202 37 62
+176 37 54
+157 32 52
+156 33 54
+138 33 68

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/descr/diabetes.rst

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+.. _diabetes_dataset:
+
+Diabetes dataset
+----------------
+
+Ten baseline variables, age, sex, body mass index, average blood
+pressure, and six blood serum measurements were obtained for each of n =
+442 diabetes patients, as well as the response of interest, a
+quantitative measure of disease progression one year after baseline.
+
+**Data Set Characteristics:**
+
+:Number of Instances: 442
+
+:Number of Attributes: First 10 columns are numeric predictive values
+
+:Target: Column 11 is a quantitative measure of disease progression one year after baseline
+
+:Attribute Information:
+    - age     age in years
+    - sex
+    - bmi     body mass index
+    - bp      average blood pressure
+    - s1      tc, total serum cholesterol
+    - s2      ldl, low-density lipoproteins
+    - s3      hdl, high-density lipoproteins
+    - s4      tch, total cholesterol / HDL
+    - s5      ltg, possibly log of serum triglycerides level
+    - s6      glu, blood sugar level
+
+Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times the square root of `n_samples` (i.e. the sum of squares of each column totals 1).
+
+Source URL:
+https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html
+
+For more information see:
+Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) "Least Angle Regression," Annals of Statistics (with discussion), 407-499.
+(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/descr/digits.rst

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+.. _digits_dataset:
+
+Optical recognition of handwritten digits dataset
+--------------------------------------------------
+
+**Data Set Characteristics:**
+
+:Number of Instances: 1797
+:Number of Attributes: 64
+:Attribute Information: 8x8 image of integer pixels in the range 0..16.
+:Missing Attribute Values: None
+:Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)
+:Date: July; 1998
+
+This is a copy of the test set of the UCI ML hand-written digits datasets
+https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits
+
+The data set contains images of hand-written digits: 10 classes where
+each class refers to a digit.
+
+Preprocessing programs made available by NIST were used to extract
+normalized bitmaps of handwritten digits from a preprinted form. From a
+total of 43 people, 30 contributed to the training set and different 13
+to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of
+4x4 and the number of on pixels are counted in each block. This generates
+an input matrix of 8x8 where each element is an integer in the range
+0..16. This reduces dimensionality and gives invariance to small
+distortions.
+
+For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.
+T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.
+L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,
+1994.
+
+|details-start|
+**References**
+|details-split|
+
+- C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their
+  Applications to Handwritten Digit Recognition, MSc Thesis, Institute of
+  Graduate Studies in Science and Engineering, Bogazici University.
+- E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.
+- Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.
+  Linear dimensionalityreduction using relevance weighted LDA. School of
+  Electrical and Electronic Engineering Nanyang Technological University.
+  2005.
+- Claudio Gentile. A New Approximate Maximal Margin Classification
+  Algorithm. NIPS. 2000.
+
+|details-end|

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/descr/linnerud.rst

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+.. _linnerrud_dataset:
+
+Linnerrud dataset
+-----------------
+
+**Data Set Characteristics:**
+
+:Number of Instances: 20
+:Number of Attributes: 3
+:Missing Attribute Values: None
+
+The Linnerud dataset is a multi-output regression dataset. It consists of three
+exercise (data) and three physiological (target) variables collected from
+twenty middle-aged men in a fitness club:
+
+- *physiological* - CSV containing 20 observations on 3 physiological variables:
+   Weight, Waist and Pulse.
+- *exercise* - CSV containing 20 observations on 3 exercise variables:
+   Chins, Situps and Jumps.
+
+|details-start|
+**References**
+|details-split|
+
+* Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris:
+  Editions Technic.
+
+|details-end|

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/descr/olivetti_faces.rst

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+.. _olivetti_faces_dataset:
+
+The Olivetti faces dataset
+--------------------------
+
+`This dataset contains a set of face images`_ taken between April 1992 and
+April 1994 at AT&T Laboratories Cambridge. The
+:func:`sklearn.datasets.fetch_olivetti_faces` function is the data
+fetching / caching function that downloads the data
+archive from AT&T.
+
+.. _This dataset contains a set of face images: https://cam-orl.co.uk/facedatabase.html
+
+As described on the original website:
+
+    There are ten different images of each of 40 distinct subjects. For some
+    subjects, the images were taken at different times, varying the lighting,
+    facial expressions (open / closed eyes, smiling / not smiling) and facial
+    details (glasses / no glasses). All the images were taken against a dark
+    homogeneous background with the subjects in an upright, frontal position
+    (with tolerance for some side movement).
+
+**Data Set Characteristics:**
+
+=================   =====================
+Classes                                40
+Samples total                         400
+Dimensionality                       4096
+Features            real, between 0 and 1
+=================   =====================
+
+The image is quantized to 256 grey levels and stored as unsigned 8-bit
+integers; the loader will convert these to floating point values on the
+interval [0, 1], which are easier to work with for many algorithms.
+
+The "target" for this database is an integer from 0 to 39 indicating the
+identity of the person pictured; however, with only 10 examples per class, this
+relatively small dataset is more interesting from an unsupervised or
+semi-supervised perspective.
+
+The original dataset consisted of 92 x 112, while the version available here
+consists of 64x64 images.
+
+When using these images, please give credit to AT&T Laboratories Cambridge.

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/descr/species_distributions.rst

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+.. _species_distribution_dataset:
+
+Species distribution dataset
+----------------------------
+
+This dataset represents the geographic distribution of two species in Central and
+South America. The two species are:
+
+- `"Bradypus variegatus" <http://www.iucnredlist.org/details/3038/0>`_ ,
+  the Brown-throated Sloth.
+
+ - `"Microryzomys minutus" <http://www.iucnredlist.org/details/13408/0>`_ ,
+   also known as the Forest Small Rice Rat, a rodent that lives in Peru,
+   Colombia, Ecuador, Peru, and Venezuela.
+
+The dataset is not a typical dataset since a :class:`~sklearn.datasets.base.Bunch`
+containing the attributes `data` and `target` is not returned. Instead, we have
+information allowing to create a "density" map of the different species.
+
+The grid for the map can be built using the attributes `x_left_lower_corner`,
+`y_left_lower_corner`, `Nx`, `Ny` and `grid_size`, which respectively correspond
+to the x and y coordinates of the lower left corner of the grid, the number of
+points along the x- and y-axis and the size of the step on the grid.
+
+The density at each location of the grid is contained in the `coverage` attribute.
+
+Finally, the `train` and `test` attributes contain information regarding the location
+of a species at a specific location.
+
+The dataset is provided by Phillips et. al. (2006).
+
+.. topic:: References
+
+ * `"Maximum entropy modeling of species geographic distributions"
+   <http://rob.schapire.net/papers/ecolmod.pdf>`_ S. J. Phillips,
+   R. P. Anderson, R. E. Schapire - Ecological Modelling, 190:231-259, 2006.

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/descr/wine_data.rst

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+.. _wine_dataset:
+
+Wine recognition dataset
+------------------------
+
+**Data Set Characteristics:**
+
+:Number of Instances: 178
+:Number of Attributes: 13 numeric, predictive attributes and the class
+:Attribute Information:
+    - Alcohol
+    - Malic acid
+    - Ash
+    - Alcalinity of ash
+    - Magnesium
+    - Total phenols
+    - Flavanoids
+    - Nonflavanoid phenols
+    - Proanthocyanins
+    - Color intensity
+    - Hue
+    - OD280/OD315 of diluted wines
+    - Proline
+    - class:
+        - class_0
+        - class_1
+        - class_2
+
+:Summary Statistics:
+
+============================= ==== ===== ======= =====
+                                Min   Max   Mean     SD
+============================= ==== ===== ======= =====
+Alcohol:                      11.0  14.8    13.0   0.8
+Malic Acid:                   0.74  5.80    2.34  1.12
+Ash:                          1.36  3.23    2.36  0.27
+Alcalinity of Ash:            10.6  30.0    19.5   3.3
+Magnesium:                    70.0 162.0    99.7  14.3
+Total Phenols:                0.98  3.88    2.29  0.63
+Flavanoids:                   0.34  5.08    2.03  1.00
+Nonflavanoid Phenols:         0.13  0.66    0.36  0.12
+Proanthocyanins:              0.41  3.58    1.59  0.57
+Colour Intensity:              1.3  13.0     5.1   2.3
+Hue:                          0.48  1.71    0.96  0.23
+OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71
+Proline:                       278  1680     746   315
+============================= ==== ===== ======= =====
+
+:Missing Attribute Values: None
+:Class Distribution: class_0 (59), class_1 (71), class_2 (48)
+:Creator: R.A. Fisher
+:Donor: Michael Marshall (MARSHALL%[email protected])
+:Date: July, 1988
+
+This is a copy of UCI ML Wine recognition datasets.
+https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data
+
+The data is the results of a chemical analysis of wines grown in the same
+region in Italy by three different cultivators. There are thirteen different
+measurements taken for different constituents found in the three types of
+wine.
+
+Original Owners:
+
+Forina, M. et al, PARVUS -
+An Extendible Package for Data Exploration, Classification and Correlation.
+Institute of Pharmaceutical and Food Analysis and Technologies,
+Via Brigata Salerno, 16147 Genoa, Italy.
+
+Citation:
+
+Lichman, M. (2013). UCI Machine Learning Repository
+[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
+School of Information and Computer Science.
+
+|details-start|
+**References**
+|details-split|
+
+(1) S. Aeberhard, D. Coomans and O. de Vel,
+Comparison of Classifiers in High Dimensional Settings,
+Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
+Mathematics and Statistics, James Cook University of North Queensland.
+(Also submitted to Technometrics).
+
+The data was used with many others for comparing various
+classifiers. The classes are separable, though only RDA
+has achieved 100% correct classification.
+(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))
+(All results using the leave-one-out technique)
+
+(2) S. Aeberhard, D. Coomans and O. de Vel,
+"THE CLASSIFICATION PERFORMANCE OF RDA"
+Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
+Mathematics and Statistics, James Cook University of North Queensland.
+(Also submitted to Journal of Chemometrics).
+
+|details-end|

env-llmeval/lib/python3.10/site-packages/sklearn/datasets/images/README.txt

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+Image: china.jpg
+Released under a creative commons license. [1]
+Attribution: Some rights reserved by danielbuechele [2]
+Retrieved 21st August, 2011 from [3] by Robert Layton
+
+[1] https://creativecommons.org/licenses/by/2.0/
+[2] https://www.flickr.com/photos/danielbuechele/
+[3] https://www.flickr.com/photos/danielbuechele/6061409035/sizes/z/in/photostream/
+
+
+Image: flower.jpg
+Released under a creative commons license. [1]
+Attribution: Some rights reserved by danielbuechele [2]
+Retrieved 21st August, 2011 from [3] by Robert Layton
+
+[1] https://creativecommons.org/licenses/by/2.0/
+[2] https://www.flickr.com/photos/vultilion/
+[3] https://www.flickr.com/photos/vultilion/6056698931/sizes/z/in/photostream/
+
+
+