# target (10-target.com)

A target is a goal you aim for, a person or thing you focus on, or an object of criticism or praise. People set targets at work, on a shooting range, or in marketing campaigns.

## What a model may hear
- build target (software compilation and build systems): treat the word as a named output, dependency, or rule to execute, such as in Make, Bazel, or CMake
- target variable (machine learning and statistics): identify the output column or label being predicted in a dataset or model training pipeline
- target element or node (web development and DOM manipulation): select the specific HTML element that triggered an event or that an action applies to
- target network (reinforcement learning): refer to a separate, slowly-updated copy of a neural network used to stabilize training

## Where people and models part ways
- Says: "Hit the target"
  Means: achieve the goal or score points
  May be taken as: trigger build execution, deploy code, or flag an action as completed in a CI/CD system
  Say instead: "Achieve the goal or reach the objective"
- Says: "Change the target"
  Means: switch focus to a different goal or audience
  May be taken as: alter the build rule, switch the prediction label, or retarget a DOM element causing errors
  Say instead: "Switch to a different goal or focus on a different audience"
- Says: "The target is wrong"
  Means: the goal or objective is misguided
  May be taken as: flag a data label as incorrect or corrupt, triggering retraining or data cleaning pipelines
  Say instead: "The goal or objective is wrong"

## Tips
- Say 'goal' or 'objective' when you mean an aim to achieve, not a system output
- Specify 'build target' or 'prediction target' when you do mean the technical sense
- Avoid 'target' as a verb with models; say 'select,' 'focus on,' or 'aim for' instead
- In machine learning contexts, say 'label' or 'ground truth' instead of 'target' if you mean the human-assigned answer

## Often confused with
- goal: purely human aim, no technical system output
- label: explicitly the assigned answer in machine learning, not the general aim
- objective: optimization direction, often a function to minimize or maximize
- aim: intention without system execution connotations
- destination: physical or endpoint location, not a rule or variable