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How to model continuous and discrete factors together?
For example, if your response variable is continuous and your predictors are some combination, you can run a linear regression model to get a decent predictive y. Or use logistical regression to model a respsonse variable that is ordinal, binary, or nominal.
Can a continuous variable be used as a discrete variable?
You can use both continuous and categorical/discrete variables as X’s in multiple regression. If the discrete variables are ordinal (i.e. Lickert Scale) then you can use them as you would any other X. If they are categorical and nominal then you will need to use dummy variables to represent their levels in the regression equation.
How to cluster datasets with discrete and continuous variables?
The idea is creating a synthetic dataset by shuffling values in the original dataset and training a classifier for separating both. During classification you will get an inter-sample distance matrix, on which you could test your favorite clustering algorithm.
How to choose between continuousification and discretization in GMM?
Choosing between continuousification or discretization depends on your dataset and what your features look like, so it’s a bit hard to say, but I advise you to read the articles I gave you on that topic. K-means obviously doesn’t make any sense, as it computes means (which are nonsensical). Same goes for GMM.
Why is the CLM called a linear model?
The CLM is a mathematical function that relates explanatory variables (either discrete or continuous) to a single continuous response variable. It is called linear because the coefficients of the terms are expressed as a linear sum. The terms themselves do not have to be linear. Model The general form of the CLM is:
How is a continuous linear model used in process modeling?
Continuous Linear Model. This is a method that gives estimates which minimize the sum of the squared distances from the observations to the fitted line or plane. See the chapter on Process Modeling for a more complete discussion on estimating the coefficients for these models.