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2. Logistic regression - Wikipedia

   en.wikipedia.org/wiki/Logistic_regression

   Logistic regression is a supervised machine learning algorithm widely used for binary classification tasks, such as identifying whether an email is spam or not and diagnosing diseases by assessing the presence or absence of specific conditions based on patient test results. This approach utilizes the logistic (or sigmoid) function to transform ...

3. Logistic distribution - Wikipedia

   en.wikipedia.org/wiki/Logistic_distribution

   In probability theory and statistics, the logistic distribution is a continuous probability distribution. Its cumulative distribution function is the logistic function, which appears in logistic regression and feedforward neural networks. It resembles the normal distribution in shape but has heavier tails (higher kurtosis).

4. Iteratively reweighted least squares - Wikipedia

   en.wikipedia.org/wiki/Iteratively_reweighted...

   IRLS is used to find the maximum likelihood estimates of a generalized linear model, and in robust regression to find an M-estimator, as a way of mitigating the influence of outliers in an otherwise normally-distributed data set, for example, by minimizing the least absolute errors rather than the least square errors.

5. Multinomial logistic regression - Wikipedia

   en.wikipedia.org/.../Multinomial_logistic_regression

   Multinomial logistic regression is known by a variety of other names, including polytomous LR, [2] [3] multiclass LR, softmax regression, multinomial logit (mlogit), the maximum entropy (MaxEnt) classifier, and the conditional maximum entropy model.

6. Conditional logistic regression - Wikipedia

   en.wikipedia.org/.../Conditional_logistic_regression

   Logistic regression as described above works satisfactorily when the number of strata is small relative to the amount of data. If we hold the number of strata fixed and increase the amount of data, estimates of the model parameters ( α i {\displaystyle \alpha _{i}} for each stratum and the vector β {\displaystyle {\boldsymbol {\beta ...

7. Regression analysis - Wikipedia

   en.wikipedia.org/wiki/Regression_analysis

   The earliest regression form was seen in Isaac Newton's work in 1700 while studying equinoxes, being credited with introducing "an embryonic linear aggression analysis" as "Not only did he perform the averaging of a set of data, 50 years before Tobias Mayer, but summing the residuals to zero he forced the regression line to pass through the ...

8. One in ten rule - Wikipedia

   en.wikipedia.org/wiki/One_in_ten_rule

   In statistics, the one in ten rule is a rule of thumb for how many predictor parameters can be estimated from data when doing regression analysis (in particular proportional hazards models in survival analysis and logistic regression) while keeping the risk of overfitting and finding spurious correlations low. The rule states that one ...

9. Ordered logit - Wikipedia

   en.wikipedia.org/wiki/Ordered_logit

   In statistics, the ordered logit model or proportional odds logistic regression is an ordinal regression model—that is, a regression model for ordinal dependent variables—first considered by Peter McCullagh. [1]