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May 28, 2012 · 31. In the linear regression model the dependent variable y is considered continuous, whereas in logistic regression it is categorical, i.e., discrete. In application, the former is used in regression settings while the latter is used for binary classification or multi-class classification (where it is called multinomial logistic regression).
54. I am trying to wrap my head around the statistical difference between Linear discriminant analysis and Logistic regression. Is my understanding right that, for a two class classification problem, LDA predicts two normal density functions (one for each class) that creates a linear boundary where they intersect, whereas logistic regression ...
$\begingroup$ So logistic regression can be formulated exactly like ADALINE (single layer neural network that uses batch/stochastic gradient descent), with the only key differences being the activation function being changed to sigmoid instead of linear, and the prediction function changing to >=0.5 with 0,1 labels instead of >=0 with -1,1 labels.
Jul 20, 2015 · The output is bounded asymptotically between $0$ and $1$, and depends on a linear model, such that when the underlying regression line has value $0$, the logistic equation is $0.5 = \frac{e^0}{1+e^0}$, providing a natural cutoff point for classification purposes.
Feb 16, 2014 · The biggest difference would be that logistic regression assumes the response is distributed as a binomial and log-linear regression assumes the response is distributed as Poisson. In fact, log-linear regression is rather different from most regression models in that the response variable isn't really one of your variables at all (in the usual sense), but rather the set of frequency counts associated with the combinations of your variables in the multi-way contingency table.
1) A logistic regression calculates the probability of an event happening based on the factors you feed into your model, and it uses a logit transform to give you those probabilities. (I will assume that you know this type of regression quite well so I will not go too much into it).
Oct 17, 2014 · The logit is a link function / a transformation of a parameter. It is the logarithm of the odds. If we call the parameter , it is defined as follows: −. The logistic function is the inverse of the logit. If we have a value, , the logistic is: +. Thus (using matrix notation where is an matrix and is a vector), logit regression is:
$\begingroup$ It may help you to read two of my answers to related questions: Difference between logit and probit models (which discusses link functions & GLiMs in general--a comment at the end specifically addresses your 1 & 3), & Difference between generalized linear models & generalized linear mixed models (which discusses how your 4 is ...
Mar 20, 2021 · Logistic regression is a supervised learning task. If you have inputs and a binary outcome then you can use logistic regression. For example, let's say I want to know the probability of a plant dying using the mass of herbicide I use as my predictor. The outcome is did the plant die (yes or no, hence binary) and the predictor would be the mass ...
Jul 12, 2015 · $\begingroup$ 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.
