The data consists of 48x48 pixel grayscale images of faces. The faces have been automatically registered so that the face is more or less centered and occupies about the same amount of space in each image. The task is to categorize each face based on the emotion shown in the facial expression in to one of seven categories (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral).
train.csv contains two columns, "emotion" and "pixels". The "emotion" column contains a numeric code ranging from 0 to 6, inclusive, for the emotion that is present in the image. The "pixels" column contains a string surrounded in quotes for each image. The contents of this string a space-separated pixel values in row major order. test.csv contains only the "pixels" column and your task is to predict the emotion column.
The training set consists of 28,709 examples. The public test set used for the leaderboard consists of 3,589 examples. The final test set, which was used to determine the winner of the competition, consists of another 3,589 examples.
This dataset was prepared by Pierre-Luc Carrier and Aaron Courville, as part of an ongoing research project. They have graciously provided the workshop organizers with a preliminary version of their dataset to use for this contest.
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Number of Instances: 150 (50 in each of three classes)
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Number of Attributes: 4 numeric, predictive attributes and the class
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Attribute Information:
- sepal length in cm
- sepal width in cm
- petal length in cm
- petal width in cm
- class: -- Iris Setosa -- Iris Versicolour -- Iris Virginica
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Missing Attribute Values: None
Summary Statistics:
| Features\Stats | Min | Max | SD | Class | Correlation |
|---|---|---|---|---|---|
| sepal length | 4.3 | 7.9 | 5.84 | 0.83 | 0.7826 |
| sepal width | 2.0 | 4.4 | 3.05 | 0.43 | -0.4194 |
| petal length | 1.0 | 6.9 | 3.76 | 1.76 | 0.9490 |
| petal width | 0.1 | 2.5 | 1.20 | 0.76 | 0.9565 |
- Class Distribution: 33.3% for each of 3 classes.
##MNIST The data files train.csv and test.csv contain gray-scale images of hand-drawn digits, from zero through nine.
Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255, inclusive.
The training data set, (train.csv), has 785 columns. The first column, called "label", is the digit that was drawn by the user. The rest of the columns contain the pixel-values of the associated image.
Each pixel column in the training set has a name like pixelx, where x is an integer between 0 and 783, inclusive. To locate this pixel on the image, suppose that we have decomposed x as x = i * 28 + j, where i and j are integers between 0 and 27, inclusive. Then pixelx is located on row i and column j of a 28 x 28 matrix, (indexing by zero).
For example, pixel31 indicates the pixel that is in the fourth column from the left, and the second row from the top, as in the ascii-diagram below.
Visually, if we omit the "pixel" prefix, the pixels make up the image like this:
000 001 002 003 ... 026 027 028 029 030 031 ... 054 055 056 057 058 059 ... 082 083 | | | | ... | | 728 729 730 731 ... 754 755 756 757 758 759 ... 782 783
The test data set, (test.csv), is the same as the training set, except that it does not contain the "label" column.
The Multi-Domain Sentiment Dataset contains product reviews taken from Amazon.com from 4 product types (domains): Kitchen, Books, DVDs, and Electronics. Each domain has several thousand reviews, but the exact number varies by domain. Reviews contain star ratings (1 to 5 stars) that can be converted into binary labels if needed. This page contains some descriptions about the data. If you have questions, please email me directly email
Unlabed instances were removed
John Blitzer, Mark Dredze, Fernando Pereira. Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. Association of Computational Linguistics (ACL), 2007.PDF
The "spam" concept is diverse: advertisements for products/web sites, make money fast schemes, chain letters, pornography...
Our collection of spam e-mails came from our postmaster and individuals who had filed spam. Our collection of non-spam e-mails came from filed work and personal e-mails, and hence the word 'george' and the area code '650' are indicators of non-spam. These are useful when constructing a personalized spam filter. One would either have to blind such non-spam indicators or get a very wide collection of non-spam to generate a general purpose spam filter.
For background on spam:
Cranor, Lorrie F., LaMacchia, Brian A. Spam! Communications of the ACM, 41(8):74-83, 1998.
(a) Hewlett-Packard Internal-only Technical Report. External forthcoming. (b) Determine whether a given email is spam or not. (c) ~7% misclassification error. False positives (marking good mail as spam) are very undesirable.If we insist on zero false positives in the training/testing set, 20-25% of the spam passed through the filter.
SPAM E-MAIL DATABASE ATTRIBUTES (in .names format)
48 continuous real [0,100] attributes of type word_freq_WORD = percentage of words in the e-mail that match WORD, i.e. 100 * (number of times the WORD appears in the e-mail) / total number of words in e-mail. A "word" in this case is any string of alphanumeric characters bounded by non-alphanumeric characters or end-of-string.
6 continuous real [0,100] attributes of type char_freq_CHAR = percentage of characters in the e-mail that match CHAR, i.e. 100 * (number of CHAR occurences) / total characters in e-mail
1 continuous real [1,...] attribute of type capital_run_length_average = average length of uninterrupted sequences of capital letters
1 continuous integer [1,...] attribute of type capital_run_length_longest = length of longest uninterrupted sequence of capital letters
1 continuous integer [1,...] attribute of type capital_run_length_total = sum of length of uninterrupted sequences of capital letters = total number of capital letters in the e-mail
1 nominal {0,1} class attribute of type spam = denotes whether the e-mail was considered spam (1) or not (0), i.e. unsolicited commercial e-mail.
For more information, see file 'spambase.DOCUMENTATION' at the [UCI Machine Learning Repositor]: (http://www.ics.uci.edu/~mlearn/MLRepository.html)