Search results
Results From The WOW.Com Content Network
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 ...
Further if the above statement for algorithm is true for every concept and for every distribution over , and for all <, < then is (efficiently) PAC learnable (or distribution-free PAC learnable). We can also say that A {\displaystyle A} is a PAC learning algorithm for C {\displaystyle C} .
It is one approach to handling the "errors in variables" problem, and is also sometimes used even when the covariates are assumed to be error-free. Linear Template Fit (LTF) [7] combines a linear regression with (generalized) least squares in order to determine the best estimator. The Linear Template Fit addresses the frequent issue, when the ...
Exact logistic regression: theory and examples. Statistics in Medicine, 14: 2143–2160. Mehta CR, Patel NR and Gray R. 1985. On computing an exact confidence interval for the common odds ratio in several 2 x 2 contingency tables. Journal of the American Statistical Association, 80(392): 969–973. Weerahandi, S. 1995.
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 ...
In statistics, generalized least squares (GLS) is a method used to estimate the unknown parameters in a linear regression model.It is used when there is a non-zero amount of correlation between the residuals in the regression model.
In computer science, a logistic model tree (LMT) is a classification model with an associated supervised training algorithm that combines logistic regression (LR) and decision tree learning. [ 1 ] [ 2 ]
Multinomial logistic regression and multinomial probit regression for categorical data. Ordered logit and ordered probit regression for ordinal data. Single index models [ clarification needed ] allow some degree of nonlinearity in the relationship between x and y , while preserving the central role of the linear predictor β ′ x as in the ...