classification - Why is logistic regression a linear classifier ...
$\begingroup$ Logistic regression is neither linear nor is it a classifier. The idea of a "decision boundary" has little to do with logistic regression, which is instead a direct probability estimation method that separates predictions from decision. $\endgroup$ –
What's the difference between logistic regression and perceptron?
2015年7月20日 · The logistic regression uses logistic function to build the output from a given inputs. Logistic function produces a smooth output between 0 and 1, so you need one more thing to make it a classifier, which is a threshold.
regression - Why RidgeClassifier can be significantly faster than ...
2022年1月1日 · Having said which, I would use multi-nomial logistic regression, which is much slower as it requires the repeated solution of a much larger set of linear equations (but can give a better classifier). On the other hand, logistic regression methods can also be fitted using Bohning's method, which uses a fixed Hessian, which would mean that you ...
Why isn't Logistic Regression called Logistic Classification?
2014年12月8日 · Logistic regression is a regression model because it estimates the probability of class membership as a (transformation of a) multilinear function of the features. Frank Harrell has posted a number of answers on this website enumerating the pitfalls of regarding logistic regression as a classification algorithm. Among them:
Boosting A Logistic Regression Model - Cross Validated
2018年2月16日 · One main difference of classification trees and logistic regression is that the former outputs classes (-1,1) while the logistic regression outputs probs. One idea is to choose the best feature X from a set of features and pick up a threshold (0.5?) to convert the probs to classes and then use a weighted logistic regression to find the next ...
Classification XGBoost vs Logistic Regression - Cross Validated
2019年2月27日 · Gradient boosting machines (the general family of methods XGBoost is a part of) is great but it is not perfect; for example, usually gradient boosting approaches have poor probability calibration in comparison to logistic regression models (see Niculescu-Mizi & Caruana (2005) Obtaining Calibrated Probabilities from Boosting for more details).
Logistic regression vs. LDA as two-class classifiers
Binary Logistic regression (BLR) vs Linear Discriminant analysis (with 2 groups: also known as Fisher's LDA): BLR : Based on Maximum likelihood estimation. LDA : Based on Least squares estimation; equivalent to linear regression with binary predictand (coefficients are proportional and R-square = 1-Wilk's lambda).
What is the difference between logistic regression and bayesian ...
2015年7月12日 · $\begingroup$ Maximum likelihood estimation does provide a point estimate of the parameters, but one can also and should provide an estimate of uncertainty by using normal-approximation justified by the large sample properties of maximum likelihood estimators.
Logistic regression classifier with non-negative weights constraint
2012年10月3日 · Bayesian logistic regression. We can use Bayesian logistic regression and set prior distributions that put no prior weight on negative coefficient values. Of course, your exact choice of prior distribution may have an influence on the inference, but since you seem to have some prior information that may be a good thing.
machine learning - If logistic regression is a linear classifier why ...
2019年6月28日 · Moreover, logistic regression (carefully programmed) will find a decision boundary in the case of perfect separation. These are some of the reasons you might want to think a little about why logistic regression might be applied and the specific sense in which you mean "failure." $\endgroup$ –