Understanding Supervised Learning
Supervised learning, a subset of machine learning, involves training algorithms on a labeled dataset. In this process, both input data and correct output are provided, which helps the model learn over time.
Key Algorithms in Supervised Learning
- Linear Regression: Linear regression predicts a continuous value. For example, it can predict the price of a house based on features like size or location. The algorithm establishes a relationship between input features and the continuous output.
- Decision Trees: Decision trees split the data into subsets based on the value of input features. It’s a tree-like structure where each node represents a feature, each branch represents a decision rule, and each leaf represents an outcome.
- Support Vector Machines (SVM): SVMs are used for classification tasks. They work by finding the hyperplane that best divides a dataset into classes. The data points closest to the hyperplane are called support vectors, and they determine the optimal position of the hyperplane.
In Conclusion
Supervised learning is a foundational aspect of machine learning. By understanding its key algorithms like linear regression, decision trees, and SVM, one can harness the power of data more effectively. As these algorithms evolve and improve, they continue to play a crucial role in various data-driven tasks and industries.
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