Labeling is a key process in supervised machine learning for image classification models, where classes are assigned to images before model training begins.
Table of Contents
Question
Assigning classes to images before training a classification model is an example of which of the following features? Select the correct option.
A. labeling.
B. feature engineering.
C. hyperparameter tuning.
D. evaluation.
Answer
A. labeling.
Explanation
In supervised machine learning, labeling refers to the process of assigning the correct class or category to each example in the training data before training the model. This is an essential step for tasks like image classification.
For an image classification model, labeling involves tagging each image with its corresponding class label, such as “dog”, “cat”, “car”, etc. The model then learns to recognize patterns and features associated with each class during training, enabling it to predict the correct class for new, unseen images.
Labeling is necessary because supervised learning algorithms require input data (e.g. images) paired with the expected output (class labels) to learn the mapping between them. Without labeled training data, the model would not know what classes exist or how to distinguish between them.
The other options are different aspects of the machine learning process:
- Feature engineering (B) involves selecting or creating relevant features from raw data to improve model performance, but is not the same as assigning class labels.
- Hyperparameter tuning (C) is the process of choosing optimal model settings, but does not involve labeling data.
- Evaluation (D) assesses a trained model’s performance on new data, which happens after labeling and training.
Therefore, assigning classes to images before training is specifically an example of labeling in supervised learning for image classification models.
Labeling refers to the process of assigning classes or categories to images (or other data) before training a classification model. This is a crucial step in supervised learning, as it provides the necessary ground truth for the model to learn from during training.
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