# 회귀와 분류

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## 노트

### 말뭉치

1. In some cases, it is possible to convert a regression problem to a classification problem.
2. Techniques of Supervised Machine Learning algorithms include linear and logistic regression, multi-class classification, Decision Trees and support vector machines.
3. Supervised learning problems can be further grouped into Regression and Classification problems.
4. Regression and classification are categorized under the same umbrella of supervised machine learning.
5. Comparing regression vs classification in machine learning can sometimes confuse even the most seasoned data scientists.
6. Both regression and classification are types of supervised machine learning algorithms, where a model is trained according to the existing model along with correctly labelled data.
7. But there are also many differences between regression and classification algorithms that you should know in order to implement them correctly and sharpen your machine learning skills.
8. Before we deep dive into understanding the differences between regression and classification algorithms.
9. next → ← prev Regression vs. Classification in Machine Learning Regression and Classification algorithms are Supervised Learning algorithms.
10. The main difference between Regression and Classification algorithms that Regression algorithms are used to predict the continuous values such as price, salary, age, etc.
11. Firstly, the important similarity – both regression and classification are categorized under supervised machine learning approaches.
12. Support vector is used for both regression and classification.
13. In this article Regression vs Classification, let us discuss the key differences between Regression and Classification.
14. Most data scientist engineers find it difficult to choose one between regression and classification in the starting stage of their careers.
15. In some cases, the continuous output values predicted in regression can be grouped into labels and change into classification models.
16. Also, this algorithm widely used because of its simplicity and the fact that it can use for both regression and classification tasks.
17. Notice one more thing that we can convert a Regression problem into Classification problem by applying some common sense.
18. If you look closely you would see an algorithm Logistic Regression mentioned under Classification.
19. Though it was a small post, yet I hope it gave you a rich information on regression vs classification comparison.
20. Yes, it might work, but logistic regression is more suitable for classification task and we want to prove that logistic regression yields better results than linear regression.
21. From the metrics, logistic regression performed much better than linear regression in classification tasks.
22. Whereas logistic regression is for classification problems, which predicts a probability range between 0 to 1.
23. The situation has gotten acute: many machine learning experts actually label logistic regression as a classification method (it is not).
24. Logistic regression, instead, is a typical tool for classification tasks (22), e.g., acting as the last classification layer in a deep neural network (23, 24).
25. Logistic regression Logistic regression is a binary model that is extensively used for object classification and pattern recognition.
26. Similar to linear regression, the circuit can also provide one-step classification of any new (unlabeled) point, which is stored in a grounded additional row of the left cross-point array.
27. If you are just starting out in machine learning, you might be wondering what the difference is between regression and classification.
28. A decision tree can be used for either regression or classification.
29. They can be used for many different tasks including regression and classification.
30. As mentioned above, Random Forests can be used for regression and classification.
31. In order to map a logistic regression value to a binary category, you must define a classification threshold (also called the decision threshold).
32. Logistic regression is a classification algorithm, used when the value of the target variable is categorical in nature.
33. Here, by the idea of using a regression model to solve the classification problem, we rationally raise a question of whether we can draw a hypothesis function to fit to the binary dataset.
34. Here’s something important to remember: although the algorithm is called “Logistic Regression”, it is, in fact, a classification algorithm, not a regression algorithm.
35. Although logistic regression is best suited for instances of binary classification, it can be applied to multiclass classification problems, classification tasks with three or more classes.
36. The significant difference between Classification and Regression is that classification maps the input data object to some discrete labels.
37. Based on the target variable in the dataset, the DataRobot automated machine learning platform automatically decides whether the task is best suited for regression or classification.