Logistic regression: Difference between revisions
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{{DISPLAYTITLE:로지스틱 회귀}} | |||
Logistic regression is a statistical and machine learning technique widely used to solve binary classification problems. This algorithm predicts the probability that the outcome variable (dependent variable) belongs to a specific class through a linear combination of independent variables. Although it is primarily applied in binary classification with labels of 0 or 1, it can be extended to multiclass classification as well. | |||
* '''Logistic''': Used in scenarios requiring dichotomous outcomes, such as pass/fail, success/failure, survival/death, or true/false. | |||
* '''Regression analysis''': Predicts future outcomes based on past trends. Since logistic regression analysis has a categorical dependent variable, it is closer to a classification model. | |||
==Functions Used== | |||
{| class="wikitable" | |||
|- | |||
!Function!!Formula | |||
|- | |||
|Sigmoid | |||
1/(1+e<big><sup>-x</sup></big>) | |||
||[[파일:Sigmoid.png|400px]] | |||
|- | |||
|하이퍼볼릭 탄젠트 | |||
tanh(x) | |||
||[[파일:Tanh.png|400px]] | |||
|} | |||
==Types of Regression Analysis== | |||
* '''Simple Regression Analysis''': Single independent variable | |||
* '''Multiple Regression Analysis''': Two or more independent variables | |||
==Advantages and Disadvantages== | |||
* '''''Advantages''''': Simple to implement and easy to interpret. | |||
** It has a relatively low risk of overfitting and is effective for binary classification. | |||
* '''''Disadvantages''''': Performs poorly with data that lacks a linear relationship. | |||
** It is challenging to apply directly to multiclass problems, where techniques like softmax regression are often required. | |||
==See Also== | |||
*[[회귀 분석|Regression Analysis]] | |||
*[[선형 회귀|Linear Regression]] | |||
Revision as of 11:53, 31 October 2024
Logistic regression is a statistical and machine learning technique widely used to solve binary classification problems. This algorithm predicts the probability that the outcome variable (dependent variable) belongs to a specific class through a linear combination of independent variables. Although it is primarily applied in binary classification with labels of 0 or 1, it can be extended to multiclass classification as well.
- Logistic: Used in scenarios requiring dichotomous outcomes, such as pass/fail, success/failure, survival/death, or true/false.
- Regression analysis: Predicts future outcomes based on past trends. Since logistic regression analysis has a categorical dependent variable, it is closer to a classification model.
Functions Used
| Function | Formula |
|---|---|
| Sigmoid
1/(1+e-x) |
400px |
| 하이퍼볼릭 탄젠트
tanh(x) |
400px |
Types of Regression Analysis
- Simple Regression Analysis: Single independent variable
- Multiple Regression Analysis: Two or more independent variables
Advantages and Disadvantages
- Advantages: Simple to implement and easy to interpret.
- It has a relatively low risk of overfitting and is effective for binary classification.
- Disadvantages: Performs poorly with data that lacks a linear relationship.
- It is challenging to apply directly to multiclass problems, where techniques like softmax regression are often required.
