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| {{DISPLAYTITLE:로지스틱 회귀}}
| | #REDIRECT [[Logistic Regression]] |
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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.
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| * '''Logistic''': Used in scenarios requiring dichotomous outcomes, such as pass/fail, success/failure, survival/death, or true/false.
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| * '''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.
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| ==Functions Used==
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| {| class="wikitable"
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| |-
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| !Function!!Formula
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| |-
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| |Sigmoid
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| 1/(1+e<big><sup>-x</sup></big>)
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| ||[[파일:Sigmoid.png|400px]]
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| |-
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| |하이퍼볼릭 탄젠트
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| tanh(x)
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| ||[[파일:Tanh.png|400px]]
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| |}
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| ==Types of Regression Analysis==
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| * '''Simple Regression Analysis''': Single independent variable
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| * '''Multiple Regression Analysis''': Two or more independent variables
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| ==Advantages and Disadvantages==
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| * '''''Advantages''''': Simple to implement and easy to interpret.
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| ** It has a relatively low risk of overfitting and is effective for binary classification.
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| * '''''Disadvantages''''': Performs poorly with data that lacks a linear relationship.
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| ** It is challenging to apply directly to multiclass problems, where techniques like softmax regression are often required.
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| ==See Also==
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| *[[회귀 분석|Regression Analysis]]
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| *[[선형 회귀|Linear Regression]]
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