100 Facts About Supervised Learning

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Here’s a list of 100 facts about supervised learning:

1. Supervised learning is a type of machine learning where the algorithm is trained on a labeled dataset.
2. Labeled data consists of input-output pairs, where the output (label) is provided for each corresponding input.
3. The goal of supervised learning is to learn a mapping from inputs to outputs based on the training data.
4. Regression and classification are two main types of supervised learning tasks.
5. In regression, the algorithm predicts a continuous output, while in classification, it predicts a discrete label.
6. Linear regression is a common algorithm used for regression tasks, modeling a linear relationship between input features and output.
7. Logistic regression is a widely used algorithm for binary classification tasks.
8. Support Vector Machines (SVMs) are versatile supervised learning algorithms used for both regression and classification.
9. Decision trees are tree-like models that make decisions based on input features, and they can be used for both regression and classification.
10. Random Forest is an ensemble learning method that combines multiple decision trees to improve accuracy and robustness.