Problems

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Target Encoding

Feature Engineering

Easy

Target encoding (also called mean encoding) is a feature engineering technique that replaces categorical values with the mean of the target variable for each category. This converts categorical features into numeric features that capture the relationship between the category and the target, making them usable by models that require numeric input.

Given a list of categorical values and corresponding target values, replace each category with the mean target value for that category.

Algorithm

For each unique category c, compute the mean of all target values where the category equals c:

\text{encoding}(c) = \frac{1}{|\{i : \text{cat}_i = c\}|} \sum_{i : \text{cat}_i = c} \text{target}_i

Replace each category in the input with its computed mean.

Return one floating-point encoding for each input category.

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Examples

Input: categories = ["cat", "dog", "cat", "dog"], targets = [1, 2, 3, 4]

Output: [2.0, 3.0, 2.0, 3.0]

Explanation: Cat targets average to 2 and dog targets average to 3.

Input: categories = ["a", "b", "c", "a", "b", "c"], targets = [1, 2, 3, 4, 5, 6]

Output: [2.5, 3.5, 4.5, 2.5, 3.5, 4.5]

Hint 1

Track a target sum and count for every category.

Hint 2

Create category means, then map the original category sequence through them.

Requirements

Compute the mean target value for each unique category
Replace each category with its computed mean
Return a list of floats with the same length as the input

Constraints

categories and targets have the same length (at least 1)
Categories are strings
Targets are numbers
Return a list of floats
Time limit: 300 ms

Try Similar Problems

Frequency Encoding Ordinal Encoding Interaction Features Cyclic Encoding Polynomial Features

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Target Encoding

Feature Engineering

Easy

Given a list of categorical values and corresponding target values, replace each category with the mean target value for that category.

Algorithm

For each unique category c, compute the mean of all target values where the category equals c:

\text{encoding}(c) = \frac{1}{|\{i : \text{cat}_i = c\}|} \sum_{i : \text{cat}_i = c} \text{target}_i

Replace each category in the input with its computed mean.

Return one floating-point encoding for each input category.

Loading visualization...

Examples

Input: categories = ["cat", "dog", "cat", "dog"], targets = [1, 2, 3, 4]

Output: [2.0, 3.0, 2.0, 3.0]

Explanation: Cat targets average to 2 and dog targets average to 3.

Input: categories = ["a", "b", "c", "a", "b", "c"], targets = [1, 2, 3, 4, 5, 6]

Output: [2.5, 3.5, 4.5, 2.5, 3.5, 4.5]

Hint 1

Track a target sum and count for every category.

Hint 2

Create category means, then map the original category sequence through them.

Requirements

Compute the mean target value for each unique category
Replace each category with its computed mean
Return a list of floats with the same length as the input

Constraints

categories and targets have the same length (at least 1)
Categories are strings
Targets are numbers
Return a list of floats
Time limit: 300 ms

Try Similar Problems

Frequency Encoding Ordinal Encoding Interaction Features Cyclic Encoding Polynomial Features

Case 1

Case 2

categories =

Accepts: array

targets =

Accepts: array

Given a list of categorical values and corresponding target values, replace each category with the mean target value for that category.

Algorithm

For each unique category c, compute the mean of all target values where the category equals c:

\text{encoding}(c) = \frac{1}{|\{i : \text{cat}_i = c\}|} \sum_{i : \text{cat}_i = c} \text{target}_i

Replace each category in the input with its computed mean.

Return one floating-point encoding for each input category.