What do you call problem with multiple target variables?

What do you call problem with multiple target variables?

F o r classification models, a problem with multiple target variables is called multi-label classification. In the realm of regression models, as a beginner, I found the nomenclature a bit confusing.

How is demand forecasting related to demand planning?

Demand forecasting is the estimation of a probable future demand for a product or service. The term is often used interchangeably with demand planning, yet the latter is a broader process that commences with forecasting but is not limited to it.

Which is an example of micro-level demand forecasting?

Demand forecasting at the micro-level can be specific to a particular industry, business, or customer segment (e.g., examining demand for natural deodorant for millennial customers in Chicago, IL). Short-term demand forecasting is usually done for a time period of less than 12 months.

How to use multi target prediction in probabilistic view?

Multi-target prediction – probabilistic view. Data are coming from distribution P(Y;X): Since we predict the value of Y for a given object x, we are interested in the conditional distribution: P(Y= yjX= x) = P(Y= y;X= x) P(X= x)

How to calculate correlation between binary and continuous variables?

The concept is to treat the ordinal or dichotomous variable as being a discrete observed counterpart to a continuous, normally distributed latent variable, and to estimate the Pearson correlation between that latent variable and the continuous variable.

Can a regression model support multiple target variables?

Multi Target Regression Machine Learning classifiers usually support a single target variable. In the case of regression models, the target is real valued, whereas in a classification model, the target is binary or multivalued. F o r classification models, a problem with multiple target variables is called multi-label classification.

How are target variables used in supervised classification?

A walk down the tree (starting from the root and then down to a leaf node) can be compared to moving from a large cluster (at the top) to smaller and smaller clusters as we near the leaf nodes. In supervised classification, the distance measure is the class of the target variable.

Which is the best model for dealing with outliers in dependent variables?

A first model to try might be Poisson regression, which is equivalent to working on a log scale (specifically, the link function is logarithmic). As perhaps implied by @Roland in a comment, it’s often true that the extreme values no longer seem outliers with the right model.