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# Mean encodings

### Week – 3 Mean encodings >>> How to Win a Data Science Competition: Learn from Top Kagglers

A lot of binary variables.
Learning to rank task.

Regularization allows us to better utilize mean encodings.
Regularization allows to make feature space more sparse.
Regularization reduces target variable leakage during the construction of mean encodings.

First split the data into train and validation, then estimate encodings on train, then apply them to validation, then validate the model on that split.
Calculate mean encodings on all train data, regularize them, then validate your model on random validation split.
Fix cross-validation split, use that split to calculate mean encodings with CV-loop regularization, use the same split to validate the model.

‘item_id_encoded1’ and ‘item_id_encoded2’ may hugely vary due to rare categories.
‘item_id_encoded1’ and ‘item_id_encoded2’ will be essentially the same only if linear regression was fitted without a regularization.
‘item_id_encoded1’ and ‘item_id_encoded2’ will be essentially the same.
#### Programming Assignment: Mean encoding implementation

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1. What can be an indicator of usefulness of mean encodings?

1 point

2. What is the purpose of regularization in case of mean encodings? Select all that apply.

1 point

3. What is the correct way of validation when doing mean encodings?

1 point

4. Suppose we have a data frame ‘df’ with categorical variable ‘item_id’ and target variable ‘target’.

We create 2 different mean encodings:

- via df[‘item_id_encoded1’] = df.groupby(‘item_id’)[‘target’].transform(‘mean’)
- via OneHotEncoding item_id, fitting Linear Regression on one hot-encoded version of item_id and then calculating ‘item_id_encoded2’ as a prediction from this linear regression on the same data.

Select the true statement.

1 point

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