As @desertnaut mentioned in the comment
No weak learner becomes strong; it is the ensemble of the weak learners that turns out to be strong
Boosting is an ensemble method that integrates multiple models(called as weak learners) to produce a supermodel (Strong learner).
Basically boosting is to train weak learners sequentially, each trying to correct its ...
I am not sure what you are really looking for but I leave here this paper here, where some intuition into that direction is provided. This paper compares the performance of a deep learning model scaling in 3 dimensions: resolution, width and depth. As depicted in their definition:
If you go to section 3.2 you will see how scaling the different dimensions ...
The sold date is a feature like any other. You can do this as follow. I am assuming the features are in a pandas data frame called df where the column date is called date. Easiest way is to use the pandas to_datetime function. Documentation is here.
def encode_dates(df, column):
df = df.copy()
df[column] = pd.to_datetime(df[column] )
df[column + ...
In Boosting, we improve the overall metrics of the model by sequentially building weak models and then building upon the weak metrics of previous models.
We start out by applying basic non-specific algorithms to the problem, which returns some weak prediction functions by taking arbitrary solutions (like sparse weights or assigning equal weights/attention). ...
You take a bunch of weak learners, each of them trained on a subset of the data.
You then just get all of them to make a prediction, and you learn how much you can trust each one, resulting in a weighted vote or other type of combination of the individual predictions.