# 회귀와 분류

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## 노트

### 말뭉치

1. In some cases, it is possible to convert a regression problem to a classification problem.[1]
2. Techniques of Supervised Machine Learning algorithms include linear and logistic regression, multi-class classification, Decision Trees and support vector machines.[2]
3. Supervised learning problems can be further grouped into Regression and Classification problems.[2]
4. Regression and classification are categorized under the same umbrella of supervised machine learning.[3]
5. Comparing regression vs classification in machine learning can sometimes confuse even the most seasoned data scientists.[4]
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.[4]
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.[4]
8. Before we deep dive into understanding the differences between regression and classification algorithms.[4]
9. next → ← prev Regression vs. Classification in Machine Learning Regression and Classification algorithms are Supervised Learning algorithms.[5]
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.[5]
11. Firstly, the important similarity – both regression and classification are categorized under supervised machine learning approaches.[6]
12. Support vector is used for both regression and classification.[7]
13. In this article Regression vs Classification, let us discuss the key differences between Regression and Classification.[8]
14. Most data scientist engineers find it difficult to choose one between regression and classification in the starting stage of their careers.[8]
15. In some cases, the continuous output values predicted in regression can be grouped into labels and change into classification models.[8]
16. Also, this algorithm widely used because of its simplicity and the fact that it can use for both regression and classification tasks.[9]
17. Notice one more thing that we can convert a Regression problem into Classification problem by applying some common sense.[10]
18. If you look closely you would see an algorithm Logistic Regression mentioned under Classification.[10]
19. Though it was a small post, yet I hope it gave you a rich information on regression vs classification comparison.[10]
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.[11]
21. From the metrics, logistic regression performed much better than linear regression in classification tasks.[11]
22. Whereas logistic regression is for classification problems, which predicts a probability range between 0 to 1.[11]
23. The situation has gotten acute: many machine learning experts actually label logistic regression as a classification method (it is not).[12]
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).[13]
25. Logistic regression Logistic regression is a binary model that is extensively used for object classification and pattern recognition.[13]
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.[13]
27. If you are just starting out in machine learning, you might be wondering what the difference is between regression and classification.[14]
28. A decision tree can be used for either regression or classification.[14]
29. They can be used for many different tasks including regression and classification.[14]
30. As mentioned above, Random Forests can be used for regression and classification.[14]
31. In order to map a logistic regression value to a binary category, you must define a classification threshold (also called the decision threshold).[15]
32. Logistic regression is a classification algorithm, used when the value of the target variable is categorical in nature.[16]
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.[16]
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.[16]
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.[16]
36. The significant difference between Classification and Regression is that classification maps the input data object to some discrete labels.[17]
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.[18]