Classification is a type of supervised machine learning that involves training a model to identify which category or class an input data point belongs to. In other words, classification is a way to automatically assign a label or category to a given piece of input data.
The process of classification typically involves feeding a large amount of labeled data into an algorithm or model, which learns to recognize patterns and relationships between different features or attributes of the data. Once the model has been trained, it can be used to predict the class of new, unlabeled data points.
Classification is used in a wide range of applications, such as image and speech recognition, text analysis, fraud detection, sentiment analysis, spam filtering, and customer segmentation, among others
Types of Classification
There are the three different types of classification: (a) Binary Classification, (b) Multi-class Classification, and (c) Multi-label Classification. The following table explains each of the classification types along with an example:
Following are the most commonly used classification algorithms in machine learning today:
- Logistic Regression
- Decision Tree
- Random Forest
- Gradient Boosting
- Support Vector Machines
- Naive Bayes Classifier
- K Nearest Neighbor (KNN)
- Artificial Neural Network
- Discriminant Analysis (LDA/QDA)
- This video by Quantra explains the concept of classification, different types and delves deeper into 4 different classification algorithms (Runtime: 7 minutes)
- In “Machine Learning Specialization” course by Andrew Ng, he discusses the concepts of traditional machine learning in detail, including classification algorithms. These videos are a great resource for understanding classification from scratch.