Target-encode a CSV categorical column

Paste CSV, pick a category and numeric target, and turn the category into its per-category target mean — with optional smoothing and leave-one-out.

About this tool

Target Mean Encoder replaces a categorical CSV column with the mean of a numeric target for each category — also called mean, impact, or likelihood encoding. It turns a high-cardinality text column (city, product, merchant, ZIP) into a single model-ready numeric feature without one-hot exploding your column count.

Pick the category column and the numeric target column, and each category becomes the average target value for its rows. Optional m-estimate smoothing blends rare categories toward the overall mean, and leave-one-out excludes each row's own target so an in-place encoding leaks less signal into the model.

Worked example

CSV:

city,churn
A,1
B,1
A,0
B,1
C,1
B,0

With category city, target churn, replace output, and 3 decimals, each city becomes its churn rate — A = 1/2 = 0.500, B = 2/3 = 0.667, C = 1/1 = 1.000:

city,churn
0.500,1
0.667,1
0.500,0
0.667,1
1.000,1
0.667,0

Switch output to append to keep the original column and add city_target_enc instead of overwriting city.

Limits & edge cases

The target column must be numeric. Non-numeric target cells are ignored when computing a category's mean; a column with no numbers at all is an error.
Smoothing uses the m-estimate blend (sum + m·prior) / (count + m), where prior is the global target mean. m = 0 gives the raw category mean.
With leave-one-out on, a category that appears in only one row has no other rows to average, so that row falls back to your unknown-category setting.
Blank category cells (and categories with no usable statistics) use the Unknown / blank category option: the global mean, NaN, or zero.
Columns can be chosen by header name, or by 1-based number when the header checkbox is off. The category and target columns must be different.
This tool encodes one category column against one target per run.

FAQ

What is target (mean) encoding and when should I use it?

Target encoding replaces each category with the average of a numeric target for that category's rows. It is handy for high-cardinality categorical features where one-hot encoding would add too many columns, giving models a compact numeric signal instead.

How does smoothing help with rare categories?

A category seen only once or twice has a noisy mean. Smoothing (the m-estimate) pulls those means toward the global target mean: the higher the smoothing weight m, the more a small category is shrunk toward the overall average, which reduces overfitting to rare values.

What does leave-one-out do?

Leave-one-out excludes each row's own target value from its category statistics before encoding that row. This reduces target leakage when you encode a training set in place, because a row can no longer "see" its own label through the encoded value.

What happens to blank or unseen categories?

Rows whose category cell is blank — or whose category has no usable target statistics — take the value from the Unknown / blank category setting. You can use the global mean (a safe default), NaN (to flag them for later handling), or zero.

Developer & Automation Access

Run it from the terminal

Same engine as this page, headless — via the gizza CLI:

gizza tool target-mean-encoder "city,churn
A,1
B,1
A,0
B,1
C,1
B,0" 'category=city' 'target=churn'

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Open it by URL

Pre-fill and auto-run this tool with query parameters — the names match the API/CLI:

https://gizza.ai/tools/target-mean-encoder/?data=city%2Cchurn%0AA%2C1%0AB%2C1%0AA%2C0%0AB%2C1%0AC%2C1%0AB%2C0&category=city&target=churn&smoothing=0&leave_one_out=true&output=replace&unknown=global-mean&decimals=4&has_header=true&delimiter=comma

Machine-readable descriptor: tool.json — title + parameters JSON Schema for agents.