Logistic regression: Difference between revisions
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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 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. | * '''Logistic''': Used in scenarios requiring dichotomous outcomes, such as pass/fail, success/failure, survival/death, or true/false. | ||
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*[[회귀 분석|Regression Analysis]] | *[[회귀 분석|Regression Analysis]] | ||
*[[선형 회귀|Linear Regression]] | *[[선형 회귀|Linear Regression]] | ||
[[분류:Data Science]] | |||
Revision as of 11:55, 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) |
|
| 하이퍼볼릭 탄젠트
tanh(x) |
|
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.
