Supervised Learning

Supervised Learning

Supervised Learning is a type of machine learning where the model learns to map input data to output labels using a labeled dataset.

What is Supervised Learning?

In supervised learning, each training example includes both the input features and the corresponding correct output (label). The goal is for the model to learn the relationship between inputs and outputs so it can predict the labels for new, unseen data.

Types of Supervised Learning

• Classification: Predicts a discrete label or category.

 Example: Email spam detection (spam or not spam).  

• Regression: Predicts a continuous value.

 Example: Predicting house prices based on features like size and location.

How Supervised Learning Works

1. Collect a labeled dataset with input-output pairs. 2. Choose an appropriate model (e.g., decision tree, support vector machine, neural network). 3. Train the model using the training data to minimize prediction errors. 4. Evaluate the model on test data to check its generalization.

Examples of Supervised Learning Algorithms

• Linear Regression
• Logistic Regression
• Decision Trees
• Support Vector Machines (SVM)
• k-Nearest Neighbors (k-NN)
• Neural Networks

Advantages of Supervised Learning

• Provides accurate predictions when sufficient labeled data is available.
• Easier to evaluate model performance with known labels.
• Suitable for a wide range of applications.

Challenges of Supervised Learning

• Requires large labeled datasets, which can be costly to obtain.
• Can overfit if the model is too complex or data is noisy.
• Performance depends heavily on the quality of labels.

Related Pages

• Unsupervised Learning
• Classification
• Regression
• Overfitting
• Underfitting
• Model Evaluation Metrics

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