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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 ...


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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 ...


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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 + ...


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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). ...


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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.


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Hard to say in general. Speaking from my own experience and by looking at which models win Kaggle competitions (see here and here), I would say tree-based models e.g. Random Forests, Decision Trees, Gradient Boosting are favorable over neural networks when working with low-dimensional data and easy interpretable features (usually simple tabular data with ...


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