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The sigmoid function is used for the two-class logistic regression, whereas the softmax function is used for the multiclass logistic regression (a.k.a. … MaxEnt, multinomial logistic regression, softmax Regression, Maximum Entropy Classifier). …

statsmodels.regression.linear_model.OLSResults.html including tests on parameters, outlier measures and specification tests http://statsmodels.sourceforge.net/devel/stats.html#residual-diagnostics-and-specification-tests Logistic … the usual families, discrete models contains besides Logit also Probit, multinomial and count regression. …

If your outcome is binary (zeros and ones), proportions of "successes" and "failures" (values between 0 and 1), or their counts, you can use Binomial distribution, i.e. the logistic regression model. … If there is more then two categories, you would use multinomial distribution in multinomial regression. …

What are the advantages of multinomial logistic regression over set of binary logistic regressions (i.e. one-vs-rest scheme)? … By set of binary logistic regression I mean that for each category $y_{i} \in Y$ we build separate binary logistic regression model with target=1 when $Y=y_{i}$ and 0 otherwise. …

Taking a wider view, we can motivate the specific form of the softmax function from the perspective of extending binary logistic regression to the case of three or more categorical outcomes. … By contrast, $\exp(x)$ is monotonic and positive for all real $x$, so the softmax result is (1) a probability vector and (2) the multinomial logistic model is identified. …

\end{align} Note that this is similar to logistic regression – a linear classifier – in the feature space defined by the $\phi_i$. … For more than two classes, we analogously get multinomial logistic (or softmax) regression. …

Background Let's start with ordinary logistic regression before moving on to the multinomial case. … Multinomial logistic regression (This has been added as a later edit.) Having recognized the value of using log odds to express chances, let's move on to the multinomial case. …

That is, such a network is just a "standard" multinomial (multi-class) classifier. And this network must have as many output neurons as classes there are. … That is, instead of using softmax to estimate a true probability distribution of the output word, a binary logistic regression (binary classification) is used instead. …

How do we do a logistic regression using the fractional outcome as the D.V.? Is there some automatic transform that can be done in glm? … Along the same lines, if there were potentially 3 or more (fractional) measurements, how would one do this for a multinomial logistic regression? …

Let me just quote SPSS Help about that issue in SPSS: Binary logistic regression models can be fitted using either the Logistic Regression procedure or the Multinomial Logistic Regression procedure. … classification Classification plots Model fitted on one set of cases to a held-out set of cases Saves predictions, residuals, and influence statistics Multinomial Logistic Regression provides the following …

If your dependent variable is categorical and your independent variables are continuous, this would be logistic regression (possibly binary, ordinal, or multinomial, depending). … Note that both logistic regression and ordinary least squares (linear) regression are special cases of the Generalized Linear Model. …

example in a convolutional network for classification where the last layer is a softmax layer, we can interpret the architecture as doing a non-linear projection onto a smaller dimension and then doing a multinomial … logistic regression (the softmax layer) on that projection. …

Longer answer: Consider a dependent variable $y$ consisting $J$ categories, than a multinomial logit model would model the probability that $y$ falls in category $m$ as: $ \mathrm{Pr}(y=m | x) = \frac … regression. …

Alan Agresti's Categorical Data Analysis, 2002) for better explanation and understanding of ordered logistic regression. … Before I answer your questions, ordered logistic regression is a case of multinomial logit models in which the categories are ordered. …

= Number of votes that the classifier casts for class $j$ for the sample $i$ that is being classified $y_i$ = Estimated class At this point I have the following questions: Q1: Do I need to use a multinomial … or can I still do this with logistic regression (e.g. in a 1-vs-all fashion)? Q2: How should I define the intermediate target variables (e.g. as in Platt's scaling) for the multi-class case? …