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Supervised Machine Learning What is Supervised Machine Learning?
Supervised machine learning is a type of artificial intelligence in which the model is trained with labeled data. Here, the algorithm is fed with input-output pairs, allowing it to learn the mapping between inputs and then its corresponding outputs.
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- How does supervised machine learning work?
- The process of supervised learning
- Types of supervised machine learning
- Partner with HPE
How does supervised machine learning work?
At the time of training with supervised machine learning, the algorithm tweaks its parameters to lessen the difference between expected and actual outputs that occur. Once trained, the model can generate predictions on previously unknown data by generalizing patterns from the training data. Common supervised learning problems include classification, which predicts a categorical label, and regression, which predicts a continuous value.
Supervised vs Unsupervised
Aspect | Supervised Learning | Unsupervised Learning |
---|---|---|
Definition | Supervised learning is a type of machine learning where the algorithm learns from labeled data, which includes both input data and corresponding output labels. The goal is to predict or classify new data based on the patterns learned from the labeled examples. | Unsupervised learning is a type of machine learning where the algorithm learns from unlabeled data. It explores the structure and patterns within the data without explicit guidance or feedback and aims to uncover hidden insights or groupings. |
Training Data | Requires labeled data (input-output pairs). | Does not require labeled data. |
Goal | Predicts or classifies based on labeled data. | Finds hidden patterns or structures in data. |
Feedback | Receives feedback during training. | No feedback during training. |
Output | Output is known and predefined. | Output is not predefined or known. |
Example Applications | Spam detection, image recognition, sentiment analysis. | Clustering, anomaly detection, dimensionality reduction. |
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