Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
The entropy concept was first introduced for discrete distributions (called Shannon entropy), which is defined as
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
If you face a relative simple image recognition problem which hasn't been studied by other people before so that no public data is available for it, it is probably less effort …
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
LightGBM support pandas columns of category type.
As a matter of fact,
this is the suggested way of handling categorical columns in LightGBM.
data[feature] = pd.Series(data[feature], dtype="category")
A LightGBM model (which is a Booster object) records categories of each categorical feature. This information is used to set categories of each categorical feature during prediction, which ensures that a LightGBM model can always handle categorical features correctly.
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
Categorical variables are very common in a machine learning project. On a high level, there are two ways to handle a categorical variable.
Drop a categorical variable if a categorical variable …
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
It is strongly suggested that you load data into a pandas DataFrame
and handle categorical variables
by specifying a dtype of "category" for those categorical variables.
df.cat_var = df.cat_var.astype …Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
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