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In statistics, a logistic model (or logit model) is a statistical model that models the log-odds of an event as a linear combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) estimates the parameters of a logistic model (the coefficients in the linear or non linear combinations). In binary…
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logistic regression model displaystyle probability function linear variables logit variable one data binary explanatory value odds distribution used beta values
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Logistic regression | is a | likelihood-ratio test | 0.90 | text |
| Logistic regression | is a | generalization of binary logistic regression to include any number of explanatory variables and any number of categories | 0.90 | text |
| Logistic regression | is a | 0-or-1 variable | 0.90 | text |
| Logistic regression | is a | important machine learning algorithm | 0.90 | text |
| Logistic regression | is a | alternative to Fisher's 1936 method | 0.90 | text |
| prediction of a customer's propensity to purchase a product or halt a subscription | instance of | It is also used in marketing applications | 0.80 | text |
| etc | instance of | It is also used in marketing applications | 0.80 | text |
| the L-BFGS method.The interpretation of the βj parameter estimates is as the additive effect on the log of the odds for a unit change in the j the explanatory variable | instance of | a quasi-Newton method | 0.80 | text |
| OpenBUGS | instance of | automatic software | 0.80 | text |
| JAGS | instance of | automatic software | 0.80 | text |
| PyMC | instance of | automatic software | 0.80 | text |
| Stan or Turing.jl allows these posteriors to be computed using simulation | instance of | automatic software | 0.80 | text |
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