Definition: Classification is a supervised machine learning technique used to predict the category or class of a data point.

Detailed Description:

Classification algorithms are trained on labeled data to learn patterns and relationships between features and class labels. Once trained, they can be used to predict the class of new, unseen data points.

Common classification algorithms include:

• Decision Trees: Trees with internal nodes representing features and leaf nodes representing class labels.
• Support Vector Machines (SVMs): Find a hyperplane to separate data points into different classes.
• Naive Bayes: Based on Bayes’ theorem, assuming independence between features.
• K-Nearest Neighbors (KNN): Classifies data points based on the majority class of their nearest neighbors.
• Neural Networks: Complex models with multiple layers of interconnected nodes.

Classification is used in various applications, such as:

• Spam filtering: Identifying spam emails.
• Image recognition: Classifying images into different categories.
• Sentiment analysis: Determining the sentiment of text.
• Medical diagnosis: Predicting diseases based on patient data.

Related Terms:

• Machine Learning
• Supervised Learning
• Decision Trees
• Support Vector Machines
• Naive Bayes
• Neural Networks

FAQs:

1. What is the difference between classification and regression?

   • Classification predicts a discrete category, while regression predicts a continuous value.

2. How is classification used in spam filtering?

   • Classification algorithms can analyze email features to identify patterns associated with spam and classify emails accordingly.

3. What are some challenges in classification?

   • Challenges include dealing with imbalanced datasets, handling noise in data, and choosing the appropriate algorithm for a given problem.