What is Supervised and Unsupervised Learning?

Supervised Learning and Unsupervised Learning are two fundamental paradigms in machine learning, each addressing different types of tasks and scenarios.

Supervised Learning:

In supervised learning, the algorithm is trained on a labeled dataset, where each example in the training data is paired with its corresponding output or target. The goal is for the algorithm to learn a mapping from inputs to outputs based on the labeled examples.

• Training Data: The training dataset consists of input-output pairs. Each input is associated with a corresponding output or label.
• Learning Objective: The objective is to learn a function that can accurately predict the output for new, unseen inputs.
• Examples: Common examples of supervised learning tasks include:
• Classification: Assigning inputs to predefined categories or classes (e.g., spam detection, image recognition).
• Regression: Predicting a continuous output or value (e.g., predicting house prices based on features).
• Evaluation: The model is evaluated on its ability to correctly predict outputs on a separate test dataset that it has not seen during training.

Unsupervised Learning:

In unsupervised learning, the algorithm works with unlabeled data, and the goal is to discover patterns, structures, or relationships within the data without explicit guidance on the desired outputs.

• Training Data: The training dataset consists of input data without corresponding labels or outputs.
• Learning Objective: The objective is to explore the inherent structure of the data, often by grouping similar data points or reducing the dimensionality of the data.
• Examples: Common examples of unsupervised learning tasks include:
• Clustering: Grouping similar data points together based on their features (e.g., customer segmentation).
• Dimensionality Reduction: Reducing the number of features or variables in the data while preserving important information.
• Evaluation: Evaluation in unsupervised learning can be more challenging, as there are no predefined outputs to compare against. Assessment often relies on the usefulness of the discovered patterns or structures.

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