In Machine Learning (ML), **labeling** and **prediction** refer to different stages in the machine learning workflow:
1. **Labeling**:
– **Definition**: Labeling is the process of assigning correct or known output values (called labels) to data points. These labels are used during the training phase of supervised learning models. For example, in an image classification task, labeling would involve tagging images with the correct category (e.g., “cat,” “dog”).
– **Purpose**: The labeled data provides the “ground truth” for training the model. The model learns to map input features to these labels.
– **Who Does It?**: Labeling is typically done by humans or automated systems, depending on the task. In supervised learning, labeled data is crucial for model accuracy.
– **Example**:
– In a dataset of customer reviews, the label might be “positive” or “negative” sentiment.
2. **Prediction**:
– **Definition**: Prediction is the process of using a trained machine learning model to estimate the output (label) for new, unseen data points. This occurs after the model has been trained on labeled data.
– **Purpose**: The goal of prediction is to generalize the model’s understanding to new inputs and generate outputs (predictions) that reflect the learned patterns.
– **Who Does It?**: The machine learning model itself makes predictions based on its learned parameters and weights.
– **Example**:
– After training a model to classify emails as spam or not spam, it can predict whether a new, unseen email is spam.
Key Differences:
– **Labeling** occurs in the training phase and provides the correct answers (supervised learning), while **prediction** happens after training when the model is applied to new data.
– **Labeling** is done by humans (or automated systems during data preparation), whereas **prediction** is performed by the trained machine learning model.
– **Labels** are used to teach the model during training, while **predictions** are the model’s attempt to estimate the label for new data.
Example (for clarity):
– In a supervised learning problem, say you’re building a spam filter:
– **Labeling**: You label emails in your dataset as “spam” or “not spam.”
– **Prediction**: After training, your model predicts whether a new incoming email is “spam” or “not spam” based on the patterns it learned during training.
In short, labeling provides the model with known answers during training, and prediction is the model’s attempt to generate answers for new, unknown data.