Supervised and unsupervised classification are two common methods used in machine learning for image and data classification.
Supervised classification involves the use of labeled training data to teach a machine learning model to classify new data. The labeled training data consists of examples where each data point is associated with a predefined class or category. The machine learning model learns to recognize patterns in the data and assign them to the appropriate class based on these predefined examples.
Unsupervised classification, on the other hand, does not use labeled training data. Instead, the algorithm must identify patterns and groupings within the data itself. The algorithm searches for similarities and differences among the data points and groups them together based on those similarities.
The major differences between supervised and unsupervised classification are as follows:
- Labeling: Supervised classification requires labeled data, while unsupervised classification does not.
- Training: Supervised classification requires training the algorithm on labeled data to create a model, while unsupervised classification requires no explicit training of the algorithm.
- Accuracy: Supervised classification tends to be more accurate, as it is based on labeled data that has been carefully curated to represent the classes. Unsupervised classification, on the other hand, maybe less accurate since it relies on the algorithm to identify patterns without prior knowledge of what the classes represent.
- Complexity: Supervised classification can be more complex, as it involves training a model using labeled data. Unsupervised classification is typically less complex since it does not require explicit training.
- Applicability: Supervised classification is best suited for situations where there are well-defined classes and a sufficient amount of labeled data. Unsupervised classification is often used when the data is unstructured or when there are no predefined classes or labels.