I am working with a regression problem where my feature vector looks like
the following:
X:
0.54 Yes No Yes Yes 1230 0.23
0.44 Yes No No Yes 300 0.80
0.90 Yes Yes No Yes 300 0.2
...
and the target variable looks like:
y:
1900.123
3200.4
100.23
...
My question is, what is an appropriate model for this type of regression?
I could always map *Yes *and *No* to the real values 1 and 0 respectively,
but would that make sense for regression with standard methods? e.g.:
Lasso
Ridge
Lasso
RandomForests
Support Vector Machines
?
Most importantly, are there any classification/regression models that
are *particularly
well suited* for handling mixed features? (i.e. discrete and real features)
Thanks,
Josh
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