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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''': 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>)
||[[File:Sigmoid.png|400x400px]]
|-
|'''Hyperbolic Tangent'''
tanh(x)
||[[File:Tanh.png|400x400px]]
|}
==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]]
[[분류:Data Science]]

Latest revision as of 12:48, 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[edit | edit source]

Function Formula
Sigmoid

1/(1+e-x)

Sigmoid.png
Hyperbolic Tangent

tanh(x)

Tanh.png

Types of Regression Analysis[edit | edit source]

  • Simple Regression Analysis: Single independent variable
  • Multiple Regression Analysis: Two or more independent variables

Advantages and Disadvantages[edit | edit source]

  • 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[edit | edit source]

분류:Data Science