Supervised Learning
Supervised learning is a fundamental paradigm in machine learning where an algorithm learns from a dataset that has been manually labeled with the correct outputs or answers. The core idea is to train a model on these input-output pairs, allowing it to learn a mapping function that can generalize and make accurate predictions on new, unseen data for which the output is unknown. This approach is broadly categorized into two main types of problems: classification, where the goal is to predict a discrete category (e.g., identifying an email as spam or not spam), and regression, where the goal is to predict a continuous value (e.g., forecasting a house price).
1.1.4.
1.1.4.1.
1.1.4.2.
1.1.4.2.1.
1.1.4.2.2.
1.1.4.2.3.
1.1.4.2.4.
1.1.4.3.
1.1.4.3.1.
1.1.4.3.2.
1.1.4.3.3.
1.1.5.1.
1.1.5.1.1.
1.1.5.1.1.1.
1.1.5.1.1.2.
1.1.5.1.2.
1.1.5.1.2.1.
1.1.5.1.2.2.
1.1.5.1.3.
1.1.5.1.4.
1.1.5.1.5.
1.1.5.1.6.
1.1.5.2.
1.1.5.2.1.
1.1.5.2.2.
1.1.5.2.2.1.
1.1.5.2.2.2.
1.1.6.1.
1.1.6.3.
1.2.1.
1.2.1.1.
1.2.1.2.
1.2.1.3.
1.2.2.1.
1.2.2.2.
1.2.2.3.
1.2.3.1.
1.2.3.1.1.
1.2.3.1.2.
1.2.3.2.
1.2.3.2.1.
1.2.3.2.2.
1.2.3.3.
1.2.4.1.
1.2.4.3.
1.2.4.3.1.
1.2.4.3.2.
1.2.4.3.3.
1.2.4.3.4.
1.2.4.3.5.
1.2.5.1.
1.2.5.1.1.
1.2.5.1.2.
1.2.5.1.3.
1.2.5.1.3.1.
1.2.5.1.3.2.
1.2.5.1.3.3.
1.2.5.1.4.
1.2.5.1.4.1.
1.2.5.1.4.2.
1.2.5.1.4.3.
1.2.5.2.
1.3.1.
1.3.1.1.
1.3.1.2.
1.3.1.2.1.
1.3.1.2.2.
1.3.1.2.3.
1.3.1.2.4.
1.3.1.3.
1.3.1.3.1.
1.3.1.3.2.
1.3.1.3.3.
1.3.1.3.4.
1.3.1.3.5.
1.3.2.
1.3.2.1.
1.3.2.2.
1.3.2.3.
1.3.3.