The Answer Key of Data, Labels
In machine learning, a label refers to the answer that a model is expected to predict.
Labels are essential in supervised learning, where models are trained on data accompanied by correct answers.
Examples of Labels
Labels are defined differently depending on the type of problem.
Binary Classification
When dividing into two classes such as Yes/No, Spam/Not Spam (0 or 1)
Multi-Class Classification
When classifying into multiple classes such as Cat, Dog, Human, Car (0, 1, 2, 3, ...)
Regression
When predicting continuous values such as housing prices (real values)
Why are Labels Important?
Labels are crucial as they define what the model should learn.
If incorrect labels are attached to the data, the model might learn incorrect patterns, making precise data labeling very important.
Next time, we will explore weights, a factor that determines how important each feature is when training a machine learning model using features and labels.
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