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gptkbp:instanceOf
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statistical analysis
Machine learning algorithm
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gptkbp:alternativeName
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Logistic model
Logit model
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gptkbp:assumes
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Independence of errors
No multicollinearity
Linearity in the logit
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gptkbp:basedOn
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gptkb:Logistic_function
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gptkbp:canBeRegularizedWith
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L1 regularization
L2 regularization
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gptkbp:category
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gptkb:Supervised_learning
gptkb:Generalized_linear_model
Predictive modeling
Classification algorithm
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gptkbp:compatibleWith
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Homoscedasticity
Normality of predictors
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gptkbp:decisionBoundary
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Linear
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gptkbp:extendsTo
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Multinomial logistic regression
Ordinal logistic regression
Regularized logistic regression
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gptkbp:featureType
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Continuous
Categorical
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gptkbp:firstDescribed
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gptkb:Pierre_François_Verhulst
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gptkbp:form
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Sigmoid function
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https://www.w3.org/2000/01/rdf-schema#label
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Logistic regression
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gptkbp:implementedIn
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gptkb:SAS
gptkb:SPSS
gptkb:scikit-learn
R
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gptkbp:introducedIn
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19th century
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gptkbp:inventedBy
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gptkb:David_Cox
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gptkbp:lossFunction
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Log loss
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gptkbp:output
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Probability
Discrete
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gptkbp:parameter
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Maximum likelihood estimation
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gptkbp:predicts
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Categorical outcome
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gptkbp:range
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0 to 1
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gptkbp:relatedTo
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gptkb:Linear_regression
gptkb:Generalized_linear_model
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gptkbp:usedFor
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Binary classification
Multiclass classification
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gptkbp:usedIn
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Finance
Marketing
Epidemiology
Social sciences
Medical research
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gptkbp:bfsParent
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gptkb:Sigmoid
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gptkbp:bfsLayer
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6
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