classification
Classification is a machine learning technique where a computer program learns to automatically sort things into different groups. After learning from pre-labeled examples (supervised learning), the program can then take new, unlabeled data points and predict which group each belongs to.
Classification supports the automation of decision-making processes. It helps in categorizing data into different classes, which can be used for further analysis or making predictions in real-time applications. The ability to accurately classify data can lead to significant improvements in decision-making, efficiency, and the development of intelligent systems that can learn from data to make predictions or recommendations[2][3].
Classification in machine learning is like sorting things into different buckets based on their characteristics. Imagine you have a basket full of different kinds of fruits and you want to organize them. You might create labels for each bucket, one for apples, one for oranges, and one for bananas. Then, you sort each fruit into the correct bucket based on what you know about them—apples are usually round and come in red or green, oranges are round and orange, and bananas are long and yellow.
In machine learning, classification works in a similar way. A computer program, which we call a model, is given a bunch of examples—like pictures of fruits with labels telling what each one is. The model looks at these examples and learns the characteristics that make an apple an apple or an orange an orange.
Once the model has learned from the examples, you can then show it a new picture of a fruit without a label, and it will use what it learned to sort that fruit into the right bucket. It will decide if the new fruit is an apple, an orange, or a banana based on the characteristics it learned from the examples.
Classification has a wide range of applications in various fields, including:
- Email Spam Detection: Classifying emails as spam or not spam[1][2].
- Medical Diagnosis: Predicting whether a patient has a certain disease based on symptoms or test results[2].
- Image Classification: Identifying the subject of an image (e.g., distinguishing between different types of animals in photos)[2].
- Fraud Detection: Detecting fraudulent activities, such as credit card fraud[2].
- Sentiment Analysis: Determining the sentiment expressed in a piece of text, such as reviews being positive, negative, or neutral[5].
Citations:
[1] https://machinelearningmastery.com/types-of-classification-in-machine-learning/
[3] https://www.simplilearn.com/tutorials/machine-learning-tutorial/classification-in-machine-learning
[4] https://www.geeksforgeeks.org/getting-started-with-classification/
[5] https://builtin.com/data-science/supervised-machine-learning-classification
[6] https://www.javatpoint.com/classification-algorithm-in-machine-learning
[7] https://towardsdatascience.com/top-machine-learning-algorithms-for-classification-2197870ff501
[8] https://www.edureka.co/blog/classification-in-machine-learning/