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Should I scale the target variable?
Yes, you do need to scale the target variable. I will quote this reference: A target variable with a large spread of values, in turn, may result in large error gradient values causing weight values to change dramatically, making the learning process unstable.
How do you find the target variable?
In general, the target variable should have a fairly uniform distribution; in the binary case, as close to a 50/50 split as possible. If the variable is skewed to either side, it will be harder for the model to evaluate the other predictor variables. If your distribution is uneven, consider oversampling your data.
Should I scale my dependent variable?
Commonly, we scale all the features to the same range (e.g. 0 – 1). In addition, remember that all the values you use to scale your training data must be used to scale the test data. As for the dependent variable y you do not need to scale it.
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.
Which is the correct approach to multioutput regression?
The direct approach to multioutput regression involves dividing the regression problem into a separate problem for each target variable to be predicted. This assumes that the outputs are independent of each other, which might not be a correct assumption.
How are decision trees used in multi target regression?
Multi target regression (MTR) using Clustering and Decision trees. For the rest of the discussion, we shall focus on a single method, that is, decision trees and ensembles of decision trees for MTR. We need to first take a look at Predictive Clustering Trees (PCT), which is the foundation on which decision trees for MTR are built on.
How are multiple regression models different from simple regression models?
A Simple regression model is one that attempts to fit a linear regression model with a single explanatory/independent variable. Multiple regression model is one that attempts to predict a dependent variable which is based on the value of two or more independent variables.