GeeksforGeeks
geeksforgeeks.org › machine learning › ml-classification-vs-regression
Classification vs Regression in Machine Learning - GeeksforGeeks
November 27, 2025 - Classification uses a decision boundary to separate data into classes, while regression fits a line through continuous data points to predict numerical values. Regression analysis determines the relationship between independent variables and ...
IBM
ibm.com › think › topics › classification-vs-regression
Classification vs Regression | IBM
November 17, 2025 - Classification and regression algorithms are also at the core of data science and predictive models. They rely on labeled data to learn the relationships between input variables (features) and output variables (targets).
Are all Machine Learning algorithms divided into Classification and Regression, not just supervised learning? - Cross Validated
I'm newbie in AI I know that Supervised Learning algorithms are divided into Classification and Regression algorithms. But is that true of all machine learning algorithms, not just Supervised Learn... More on stats.stackexchange.com
Regression vs classification
Stars are a good example of a place where you don't want to use regression. Stars are ordered, but in order for regression to make sense you also need to believe that the difference between a 1 and 2 star rating is the same size as the difference between a 3 and 4 star rating. This is usually not true for star ratings (and in similar cases "size of the difference" may not even make sense). There are "ordinal regression" techniques that are appropriate for this type of data, but they tend to look a lot more like classification than L2 regression. However, the point that the paragraph is making is that you often get better results by binning a continuous variable and classifying the bin than you do by trying to regress it directly, regardless of whether or not that is the "right" thing to do. It is true that this does often work quite well. For an extreme example look at the pixel-rnn paper ( https://arxiv.org/pdf/1601.06759v2.pdf ), where they treat pixel intensities (which should "obviously" be regressed) as a classification problem and get great results. More on reddit.com
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June 29, 2016Andrew Ng's (new) Machine Learning course on Coursera is no longer free??
No notebooks, labs, quizzes, practice without monthly subscription More on reddit.com
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June 15, 2022Classification or Regression
Example could be if you’re wanting to see if someone has a disease like diabetes, you use classification, where it gives you discrete values of 0 or 1.
If you want to analyze past stock prices of a company and predict future stock prices accordingly, then you use regression.
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Simplilearn
simplilearn.com › home › resources › ai & machine learning › regression vs. classification in machine learning for beginners
Regression vs. Classification in Machine Learning for Beginners | Simplilearn
1 month ago - In this article, we examine regression versus classification in machine learning, including definitions, types, differences, and uses. To learn more, click here.
Coursera
coursera.org › coursera articles › data › ai and machine learning › classification vs. regression in machine learning: what’s the difference?
Classification vs. Regression in Machine Learning: What’s the Difference? | Coursera
February 12, 2026 - Regression in machine learning is a technique for predicting a continuous outcome from input variables. You can use classification models for tasks with clear, discrete outcomes, and regression models for tasks with several input variables to ...
Applied AI Blog
appliedaicourse.com › home › machine learning › classification vs regression in machine learning
Classification vs Regression in Machine Learning
October 24, 2024 - Classification tasks use metrics ... differentiate between categories. Regression in machine learning is a supervised learning technique used to predict continuous values based on input data....
Analytics Vidhya
analyticsvidhya.com › home › regression vs classification in machine learning explained!
Regression vs Classification in Machine Learning Explained!
September 12, 2024 - ... A. Data types · B. Objectives · C. Accuracy requirements · Regression algorithms predict continuous value from the provided input. A supervised learning algorithm uses real values to predict quantitative data like income, height, weight, ...
E&ICT Academy
eicta.iitk.ac.in › home › knowledge hub › machine learning › supervised learning: classification and regression methods
Supervised Learning: Classification and Regression Methods
March 10, 2025 - In summary, supervised learning encompasses various techniques for classification and regression tasks. Logistic regression, decision trees, support vector machines, Naive Bayes classifiers, and k-nearest neighbors are commonly used for classification. These popular regression methods include linear regression, decision trees, support vector regression, neural networks, and gradient boosting.
Udacity
udacity.com › blog › 2025 › 02 › regression-vs-classification-key-differences-and-when-to-use-each.html
Regression vs Classification - Key Differences and When to Use Each | Udacity
February 27, 2025 - While not perfectly accurate, these models can provide insights into potential future stock values. ... Classification is a core machine learning task that focuses on assigning data points to predefined categories or classes.
Codecademy
codecademy.com › article › regression-vs-classification
Regression vs. Classification | Codecademy
One way of categorizing machine learning algorithms is by using the kind output they produce. In terms of output, two main types of machine learning models exist: those for regression and those for classification.
Enjoy Algorithms
enjoyalgorithms.com › blogs › classification-and-regression-in-machine-learning
Classification and Regression in Machine Learning
Both classification and regression in machine learning deal with the problem of mapping a function from input to output. However, in classification problems, the output is a discrete (non-continuous) class label or categorical output, whereas, in regression problems, the output is continuous.
Medium
medium.com › @mangeshsalunke1309 › regression-vs-classification-in-machine-learning-35859262eabd
Regression vs Classification in Machine Learning | by Mangesh Salunke | Medium
June 17, 2025 - The goal is to predict a continuous value as the output, based on the input variables. Predicting Continuous Values: Unlike classification, which assigns inputs to discrete classes, regression models predict continuous values.
Medium
medium.com › @nimrashahzadisa064 › supervised-machine-learning-classification-and-regression-c145129225f8
Supervised Machine Learning: Classification and Regression | by Nimra Shahzadi | Medium
May 29, 2023 - Classification algorithms help in assigning labels or categories to new instances, while regression algorithms enable us to make continuous predictions. By understanding the principles and applications of these techniques, we can leverage the ...
Towards Data Science
towardsdatascience.com › home › latest › the difference between classification and regression in machine learning
The Difference Between Classification and Regression in Machine Learning | Towards Data Science
January 27, 2025 - The similarity between the 2 tasks is that they both are a form of supervised learning. Both regression and classification problems fall into the supervised learning category. Each task involves developing a model that learns from historical data which enables it to make predictions on new instances that we do not have answers for.
ProjectPro
projectpro.io › blog › classification vs. regression algorithms in machine learning
Classification vs. Regression Algorithms in Machine Learning M
October 28, 2024 - This means if the same input can ... classification problem. ... Regression is a supervised machine learning algorithm used to predict the continuous values of output based on the input....
Galaxy
training.galaxyproject.org › training-material › topics › statistics › tutorials › classification_regression › tutorial.html
Statistics and machine learning / Machine learning: classification and regression / Hands-on: Machine learning: classification and regression
April 8, 2025 - For classification, the targets are integers. However, when the targets in a dataset are real numbers, the machine learning task becomes regression. Each sample in the dataset has a real-valued output or target.
Springboard
springboard.com › blog › data science › regression vs. classification in machine learning: what’s the difference?
Regression vs. Classification in Machine Learning: What's the Difference?
September 28, 2023 - The most significant difference between regression vs classification is that while regression helps predict a continuous quantity, classification predicts discrete class labels. There are also some overlaps between the two types of machine learning ...
Towards AI
pub.towardsai.net › regression-vs-classification-in-machine-learning-why-most-beginners-get-this-wrong-m004-7e01b32602ec
Regression vs Classification in Machine Learning — Why Most Beginners Get This Wrong | M004 | by Mehul Ligade | Towards AI
May 15, 2025 - I had treated it like a regression problem when what I really wanted was classification. That mismatch between goal and framing wasted weeks of iteration. Since then, I always start with the same question: “What decision is this model helping someone make?” That almost always leads me to the right type of problem. ... Machine Learning is not about throwing algorithms at data.
MachineLearningMastery
machinelearningmastery.com › home › blog › difference between classification and regression in machine learning
Difference Between Classification and Regression in Machine Learning - MachineLearningMastery.com
May 22, 2019 - Fundamentally, classification is about predicting a label and regression is about predicting a quantity. I often see questions such as: How do I calculate accuracy for my regression problem?
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All unsupervised algorithms, e.g.
- clustering,
- dimension reduction (PCA, t-sne, autoencoder,...),
- missing value imputation,
- outlier detection,
- ...
Some of them might internally use regression or classification elements, but the algorithm itself is neither.
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No, it's much broader than that. You should at least read about the following:
- Clustering
- Dimensionality Reduction
- Reinforcement Learning
Svitla Systems
svitla.com › home › articles › regression vs classification in machine learning
Regression vs Classification in Machine Learning
September 10, 2020 - Classification is used to determine whether an entity with certain input parameters belongs to a class within the existing characteristics of the input values. Both regression and classification are very often and effectively applied in machine ...