Численные методы обучения по прецедентам (практика, В.В. Стрижов)/Коллекция реальных данных

Материал из MachineLearning.

Основная статья: Численные методы обучения по прецедентам (практика, В.В. Стрижов)

Коллекция реальных данных для выполнения работ по курсу Численные методы обучения по прецедентам находится по адресу https://svn.code.sf.net/p/mvr/code/data/

Measured data collection https://svn.code.sf.net/p/mvr/code/data/ contains collection of data for regression, classification, ranking and clustering.

Title: Croatian power plants waste measurements

Link: CroatianPPs.csv, CroatianPPs.xls
Abstract:
Characteristics: multivariate
Attributes: see the columns of the data file
Scales: real
Problem: alternatives ranking
Number of samples: 8
Number of features: 12
Source: DOI: 10.1016/j.energy.2011.04.030
Citation: Strijov V. et al. Integral indicator of ecological impact of the Croatian thermal power plants // Energy, 2011. Vol. 36(7). P. 4144-4149.

Title: Diabets study

Link: Diabets_LARS.csv
Abstract:
Characteristics: multivariate
Attributes: see the columns of the data file
Scales: real
Problem: regression
Number of samples: 442
Number of features: 10
Source: http://www-stat.stanford.edu/~imj/WEBLIST/2004/LarsAnnStat04.pdf
Citation: B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani. Least angle regression // Annals of statistics. Volume 32, Number 2 (2004), 407-499.

Title: Electric cars comparisons

Link: ElectricCarsComparisonNamed.csv, ElectricCarsComparison.xls
Abstract:
Characteristics: multivariate
Attributes: see the columns of the data file
Scales: real, nominal
Problem: alternatives ranking
Number of samples: 20
Number of features: 7
Source: http://www.electriccarsite.co.uk/uk-electric-car-comparisons
Citation:

Title: Energy consumption

Link: EnergyConsumption.xls
Abstract:
Characteristics: multivariate, time series
Attributes: see the columns of the data file
Scales: real, nominal
Problem: regression, time series forecasting
Number of samples: 8760
Number of features: 4
Source:
Citation: Сандуляну Л.Н., Стрижов В.В. Выбор признаков в авторегрессионных задачах прогнозирования // Информационные технологии, 2012, 7 — 11-15.

Title: Hungarian Cash Loan

Link: HungarianCashLoan.csv, HungarianCashLoan.xls, HungarianCashLoanUSD.csv
Abstract:
Characteristics: multivariate
Attributes: see the columns of the data file
Scales: real, nominal, binary
Problem: classification (last column is the target variable)
Number of samples: 6269
Number of features: 49
Source:
Citation:

Title: MMRO 15 contest OTP

Link: Contest_MMRO15_OTP.xls
Abstract:
Characteristics: multivariate
Attributes: see the columns of the data file
Scales: real, nominal, binary
Problem: classification
Number of samples: 15223
Number of features: 52
Source: http://poligon.machinelearning.ru/Contests/Card.aspx?synonim=otp
Citation:

Title: Hybrid cars comparisons

Link: HybridCarsComparison.xls
Abstract:
Characteristics: multivariate
Attributes: see the columns of the data file
Scales: real, nominal
Problem: alternatives ranking
Number of samples: 20
Number of features: 7
Source: http://www.buzzle.com/articles/hybrid-cars-comparison.html
Citation:

Title: Iris Data Set

Link: IrisClassification.txt, IrisFromUCI.csv
Abstract: This is perhaps the best known database to be found in the pattern recognition literature. Dataset can be found at the UCI machine learning repository.
Characteristics: multivariate
Attributes: see description in http://archive.ics.uci.edu/ml/datasets/Iris
Scales: real
Problem: multiclass classification
Number of samples: 150
Number of features: 4
Source: http://archive.ics.uci.edu/ml/datasets/Iris
Citation:

Title: Wine Data Set

Link: ItalianWines3Classes.csv, ItalianWines3Classes_Readme.txt
Abstract: These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines.
Characteristics: multivariate
Attributes: see description in ItalianWines3Classes_Readme.txt
Scales: real
Problem: multiclass classification
Number of samples: 178
Number of features: 13
Source: http://archive.ics.uci.edu/ml/datasets/Wine
Citation:

Title: Mortage and auto loan

Link: MortgageAndAutoLoan.csv, MortgageAndAutoLoan.xls,MortgageAndAutoLoan_Readme.txt
Abstract:
Characteristics: multivariate
Attributes: see description in MortgageAndAutoLoan_Readme.txt
Scales: real
Problem: classification
Number of samples: 1891
Number of features: 18
Source:
Citation:

Title: Sugar prices

Link: SuigarPriceNYBOT.csv, SuigarPriceNYBOT.xls
Abstract:
Characteristics: univariate, time series
Attributes: time
Scales: datetime
Problem: regression, time series forecasting
Number of samples: 1903
Number of features: 1
Source:
Citation:

Title: Turkish electricity consumption

Link: TurkElectricityConsumption.csv, TurkElectricityConsumption.xls, TurkElectricityConsumption_Readme.txt
Abstract:
Characteristics: multivariate, time series
Attributes: see description in TurkElectricityConsumption_Readme.txt
Scales: real
Problem: regression, time series forecasting
Number of samples: 1903
Number of features: 7
Source:
Citation:

Title: White bread prices

Link: WhiteBreadPrices.csv, WhiteBreadPricesSep.xls, WhiteBreadPrices_Readme.txt
Abstract:
Characteristics: univariate, time series
Attributes: see description in WhiteBreadPrices_Readme.txt
Scales: real
Problem: regression, time series forecasting
Number of samples: 195
Number of features: 1
Source:
Citation:

Title: Face profile multiclass collection

Link: FaceProfile dir
Abstract:
Characteristics: images
Attributes: each file in dir FaceProfile is an object. Class labels (A,B,C,D,E,F,G) are the substrings of filenames
Scales: real
Problem: classification
Number of samples: 45
Number of features:
Source:
Citation:

Title: European option prices and volatility smile

Link: OptionDataRS.xls
Abstract:
Characteristics: multivariate
Attributes: see the columns of the data file
Scales: real
Problem: regression
Number of samples: 26
Number of features: 18
Source:
Citation:

Title: Sale dymanics in a supermarket

Link: SupermarketSales.txt
Abstract:
Characteristics: multivariate
Attributes: columns: ProductID, time, volume, price
Scales: real
Problem: regression
Number of samples: 167289
Number of features: 4
Source:
Citation:

Title: KNN Forecasting

Link: k-NNForecasting.xls
Abstract: need docs
Characteristics:
Attributes:
Scales:
Problem:
Number of samples:
Number of features
Source:
Citation:

Title: South African heart disease

Link: SAheart.txt, SAHD.txt, SAheart.info
Abstract:
Characteristics: multivariate
Attributes: see description in SAheart.info
Scales: real, nominal
Problem: classification
Number of samples: 462
Number of features: 10
Source: http://www-stat.stanford.edu/~tibs/ElemStatLearn/data.html
Citation: http://strijov.com/papers/Skipor10Method.pdf

Title: Combustion pressure in diesel engine

Link: PressureCurves.csv
Abstract:
Characteristics: multivariate
Attributes: see description in http://svn.code.sf.net/p/mlalgorithms/code/Ivkin2010ForecastingO_2/doc/ivkin10forecastingO_2.pdf
Scales: real, nominal
Problem: regression
Number of samples: 122
Number of features: 7220
Source:
Citation:

Title: Income vs Lifespan

Link: IncomeVsLifespan.xls
Abstract:
Characteristics: multivariate
Attributes: see the columns of the data file
Scales: real
Problem: regression
Number of samples: 193
Number of features: 7
Source:
Citation:

Источник — «http://machinelearning.ru/wiki/index.php?title=%D0%A7%D0%B8%D1%81%D0%BB%D0%B5%D0%BD%D0%BD%D1%8B%D0%B5_%D0%BC%D0%B5%D1%82%D0%BE%D0%B4%D1%8B_%D0%BE%D0%B1%D1%83%D1%87%D0%B5%D0%BD%D0%B8%D1%8F_%D0%BF%D0%BE_%D0%BF%D1%80%D0%B5%D1%86%D0%B5%D0%B4%D0%B5%D0%BD%D1%82%D0%B0%D0%BC_%28%D0%BF%D1%80%D0%B0%D0%BA%D1%82%D0%B8%D0%BA%D0%B0%2C_%D0%92.%D0%92._%D0%A1%D1%82%D1%80%D0%B8%D0%B6%D0%BE%D0%B2%29/%D0%9A%D0%BE%D0%BB%D0%BB%D0%B5%D0%BA%D1%86%D0%B8%D1%8F_%D1%80%D0%B5%D0%B0%D0%BB%D1%8C%D0%BD%D1%8B%D1%85_%D0%B4%D0%B0%D0%BD%D0%BD%D1%8B%D1%85»

Категория: Учебные курсы