Classification Definition: Classification is the supervised learning task of predicting a categorical class label from input data. Each example in the dataset belongs to one of a predefined set of classes. Characteristics: Outputs are discrete. The goal is to assign each input to a single class. Classes can be binary (two classes) or multiclass (more than two classes). Examples: Classifying emails as spam or not spam (binary classification). Classifying iris flowers into one of three species (multiclass classification). Types of Classification: Binary Classification: Distinguishing between exactly two classes. Multiclass Classification: Distinguishing among more than two classes. Multilabel Classification: Assigning multiple class labels to each instance. Key Concepts: The class labels are discrete and come from a finite set . Often expressed as a yes/no question in binary classification (e.g., “Is ...
What is Supervised Learning? · Definition: Supervised learning involves training a model on a labeled dataset, where the input data (features) are paired with the correct output (labels). The model learns to map inputs to outputs and can predict labels for unseen input data. · Goal: To learn a function that generalizes well from training data to accurately predict labels for new data. · Types: · Classification: Predicting categorical labels (e.g., classifying iris flowers into species). · Regression: Predicting continuous values (e.g., predicting house prices). Key Concepts: · Generalization: The ability of a model to perform well on previously unseen data, not just the training data. · ...