# 회귀와 분류

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## 노트

### 말뭉치

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

### 소스

- ↑ Difference Between Classification and Regression in Machine Learning
- ↑
^{2.0}^{2.1}Supervised Machine Learning - GeeksforGeeks - ↑ Regression Versus Classification Machine Learning: What’s the Difference?
- ↑
^{4.0}^{4.1}^{4.2}^{4.3}Regression vs Classification in Machine Learning: What is The Difference? - ↑
^{5.0}^{5.1}Regression vs. Classification in Machine Learning - ↑ Regression Vs Classification in Machine Learning: Difference Between Regression and Classification
- ↑ An in-depth guide to supervised machine learning classification
- ↑
^{8.0}^{8.1}^{8.2}Regression vs Classification - ↑ Machine Learning For Beginners
- ↑
^{10.0}^{10.1}^{10.2}Regression vs Classification – No More Confusion !! - ↑
^{11.0}^{11.1}^{11.2}Why Linear Regression is not suitable for Classification - ↑ Classification vs. Prediction
- ↑
^{13.0}^{13.1}^{13.2}One-step regression and classification with cross-point resistive memory arrays - ↑
^{14.0}^{14.1}^{14.2}^{14.3}What is the difference between classification and regression? - ↑ Machine Learning Crash Course
- ↑
^{16.0}^{16.1}^{16.2}^{16.3}Logistic Regression For Machine Learning and Classification - ↑ Difference Between Classification and Regression (with Comparison Chart)
- ↑ DataRobot Artificial Intelligence Wiki