Scikit-Learn
32
coefficients
for
multiple
regression
problems jointly.
Elastic-Net
The Elastic-Net is a regularized regression
method that linearly combines both
penalties i.e. L1 and L2 of the Lasso and
Ridge regression methods. It is useful
when there
are multiple correlated
features.
Multi-task Elastic-Net
It is an Elastic-Net model that allows to fit
multiple regression problems jointly
enforcing the selected features to be same
for all the regression problems, also called
tasks
Linear Regression
It is one of the best statistical models that studies the relationship between a dependent
variable (Y) with a given set of independent variables (X).
The relationship can be
established with the help of fitting a best line.
sklearn.linear_model.LinearRegression
is the module used to implement linear
regression.
Parameters
Following table consists the parameters used by
Linear Regression
module:
Parameter
Description
fit_intercept
: Boolean, optional, default
True
Used to calculate the intercept for the model. No
intercept will be used in the calculation if this set
to false.
normalize
: Boolean, optional, default False
If this parameter is set to True, the regressor X will
be
normalized
before
regression.
The
normalization will be done by subtracting the mean
and dividing it by L2 norm. If
fit_intercept = False
,
this parameter will be ignored.
copy_X
: Boolean, optional, default True
By default, it is true which means X will be copied.
But if it is set to false, X may be overwritten.
n_jobs
:
int
or
None,
optional(default=None)
It represents the number
of jobs to use for the
computation.
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