Thanks Sole and Gael, I'll try both ways. Are the two methods fundamentally
different, or will they give me similar results?
Also, the majority of MI analysis I've seen with continuous variables
discretize the data into arbitrary bins. Is this procedure actually valid?
I'd think by discretizing con
For estimating mutual information on continuous variables, have a look at the
corresponding package
https://pypi.org/project/mutual-info/
G
On Wed, Feb 01, 2023 at 02:32:03PM +0100, m m wrote:
> Hello,
> I have two continuous variables (heart rate samples over a period of time),
> and
> would
Hey,
My understanding is that with sklearn you can compare 2 continuous variables
like this:
mutual_info_regression(data["var1"].to_frame(), data["var"],
discrete_features=[False])
Where var1 and var are continuous.
You can also compare multiple continuous variables against one continuous
va
Hello,
I have two continuous variables (heart rate samples over a period of time),
and would like to compute mutual information between them as a measure of
similarity.
I've read some posts suggesting to use the mutual_info_score from
scikit-learn but will this work for continuous variables? One
