Численные методы обучения по прецедентам (практика, В.В. Стрижов)/Примеры

Материал из MachineLearning.

Содержание 1 Title: Phosphor curve differences 2 Title: Classification using logistic regression 3 Title: Generalized linear models example 4 Title: Income vs Lifespan illustration 5 Title: Indexation demo 6 Title: Least Squares Linear Fit 7 Title: Linear model generation 8 Title: Volatility smile modelling 9 Title: Logistic regression example 10 Title: Overfitting curve 11 Title: Receiver Operating Characteristic example 12 Title: Report example 13 Title: SSA forecasting 14 Title: Time series forecasting 15 Title: Features combinations searching 16 Title: Distance to curve 17 Title: Exponential smoothing 18 Title: LARS illustration 19 Title: Linear model convergency

Title: Phosphor curve differences

• Folder: CurveDifferences
• Description: Phosphor curves 0-1 classification illustration. 0-class curve is blue coloured, 1-class curve is red coloured. See more detailed description at demo_curve_differences.html
• Demo script: demo_curve_differences

Title: Classification using logistic regression

• Folder: DataGenerationExample
• Description: This demo shows how to compute a separation hyperplane to classify objects. The hyperplane separates one class of objects from another. See more detailed description at demoDataGen.html
• Demo script: demoDataGen

Title: Generalized linear models example

• Folder: GLM
• Description: This demo shows how to deal with the different hypothesis on data generation. The data generation hypothesis here is an assumption about the link function. See more detailed description at demo_GLM.html
• Demo script: demo_GLM

Title: Income vs Lifespan illustration

• Folder: IncomeVsLifeSpan
• Description: Demo illustrates dependence between income and lifespan by countries.
• Demo script: IncomeVsLifespan

Title: Indexation demo

• Folder: Indexation
• Description: This demo shows how to use indices of sorted sets and how to recover the unsorted set from the sorted one. See more detailed description at demo_indexation.html
• Demo script: demo_indexation

Title: Least Squares Linear Fit

• Folder: LeastSquares
• Description: It shows how to use minimal least squares method to fit data. See more detailed description at demo_least_squares_fit.html
• Demo script: demo_least_squares_fit

Title: Linear model generation

• Folder: LinearModelGeneration
• Description: The problem of model generation is illustrated with a linear regression model of a single variable. See more detailed description at LinModelGen.html
• Demo script: LinModelGen

Title: Volatility smile modelling

• Folder: LinfitOptions
• Description: A volatility smile modeling is one of the actual problems in finance mathematics. Here is an example how to make a regression model of the volatility smile using historical data of an option. See more detailed description at demo_linfit_options.html
• Demo script: demo_linfit_options

Title: Logistic regression example

• Folder: LogisticRegression
• Description: The Newton-Raphson algorithm is used to obtain the optimal parameters of the regression model. See more detailed description at demo_logistic_regression.html
• Demo script: demo_logistic_regression

Title: Overfitting curve

• Folder: OverfittingCurve
• Description: Demo illustrates errors on learn (blue curve) and test (red curve) depending on model complexity. The test error increasing shows overfitting.
• Demo script: illustrateOvetfitting

Title: Receiver Operating Characteristic example

• Folder: PlotROC
• Description: ROC analysis provides tools to select possibly optimal models and to discard suboptimal ones independently from (and prior to specifying) the cost context or the class distribution. ROC analysis is related in a direct and natural way to cost/benefit analysis of diagnostic decision making. See more detailed description at demo_ROC.html
• Demo script: demo_ROC

Title: Report example

• Folder: ReportExample
• Description: Report description, place it in the next line after the title. Below this description the Content section will be generated automatically. See more detailed description at report_example.html
• Demo script: report_example

Title: SSA forecasting

• Folder: SSAForecast
• Description: Demo illustrates SSA time series forecasting
• Demo script: demo_ssa_forecast

Title: Time series forecasting

• Folder: TargetFunction
• Description: Tune a linear regression model using a common optimization algorithm. See more detailed description at demo_target_function.html
• Demo script: demo_target_function

Title: Features combinations searching

• Folder: Counters
• Description: Demo searches all features combinations for feature selection problem. See more detailed description at demo_cntfeatures.html
• Demo script: demo_cntfeatures

Title: Distance to curve

• Folder: DistanceToCurve
• Description: This demo shows how to find a distance between a point and a curve in Euclidean space.
• Demo script: distance2curve

Title: Exponential smoothing

• Folder: ExponentialSmoothing
• Description: Demo shows time series filtering using exponential smoothing method. See more detailed description at ExponentialSmoothing.html
• Demo script: maxim_altimetr

Title: LARS illustration

• Folder: LARS
• Description: Demo illustrates LARS algorithm for feature selection problem. See more detailed description at demo_lars.html
• Demo script: demo_lars

Title: Linear model convergency

• Folder: LinearModelConvergency
• Description: Demo shows regression model residuals variance depending on features/objects number.
• Demo script: illustrations