Contents
How many binary classifications are there?
The One-vs-Rest strategy splits a multi-class classification into one binary classification problem per class. The One-vs-One strategy splits a multi-class classification into one binary classification problem per each pair of classes.
What is classification of series?
An univariate time series is an ordered set of real values, while a M dimensional multivariate time series consists of M different univariate time series with the same length. A Time Series Classification problem is a Classification problem where the objects of the dataset are univariate or multivariate time series.
What are some examples of problems with binary classification?
Typical binary classification problems include: Medical testing to determine if a patient has certain disease or not; Quality control in industry, deciding whether a specification has been met; In information retrieval, deciding whether a page should be in the result set of a search or not.
How to cross validate binary classification on time series data?
You can think of state models for time series like Multi State Modelling (MSM) or Hidden Markov Models ( if data has markov property). In these state models you can have beginning reading, inflow, outflow as external variables and then you can build your model. Thanks for contributing an answer to Cross Validated!
What’s the difference between prevalence and binary classification?
An important distinction is between metrics that are independent of how often each category occurs in the population (the prevalence ), and metrics that depend on the prevalence – both types are useful, but they have very different properties.
Why are the two groups in binary classification not symmetric?
In many practical binary classification problems, the two groups are not symmetric, and rather than overall accuracy, the relative proportion of different types of errors is of interest.