logistic-regression

I'm using scikit learn's Logistic Regression for a multiclass problem. logit = LogisticRegression(penalty='l1') logit = logit.fit(X, y) I'm interested in which features are driving this decision. logit.coef_ The above gives me a beautiful dataframe in (n_classes, n_features) format, but all the classes and feature names are gone. With features, that's okay, because making the assumption that they're indexed the same way as I passed them in seems safe... But with classes, it's a problem, since I never explicitly passed in the classes in any order. So which class do coefficient sets (rows in the

I am using a Logistic Regression (in scikit) for a binary classification problem, and am interested in being able to explain each individual prediction. To be more precise, I'm interested in predicting the probability of the positive class, and having a measure of the importance of each feature for that prediction. Using the coefficients (Betas) as a measure of importance is generally a bad idea as answered here , but I'm yet to find a good alternative. So far the best I have found are the following 3 options: Monte Carlo Option : Fixing all other features, re-run the prediction replacing the

I am running a logistic regression with a binary outcome variable on data that has been multiply imputed using MICE. It seems straightforward to pool the coefficients of the glm model: imp=mice(nhanes2, print=F) imp$meth fit0=with(data=imp, glm(hyp~age, family = binomial)) fit1=with(data=imp, glm(hyp~age+chl, family = binomial)) summary(pool(fit1)) However, I can't figure out a way to pool other output generated by the glm. For instance, the glm function produces AIC, Null deviance and Residual deviance that can be used for model testing. pool(summary(fit1)) ## summary of imputation 1 : Call:

I want to find Lethal Dose ( LD50 ) with its confidence interval in R . Other softwares line Minitab, SPSS, SAS provide three different versions of such confidence intervals. I could not find such intervals in any package in R (I also used findFn function from sos package). How can I find such intervals? I coded for one type of intervals based on Delta method (as not sure about it correctness) but would like to use any established function from R package. Thanks MWE: dose <- c(10.2, 7.7, 5.1, 3.8, 2.6, 0) total <- c(50, 49, 46, 48, 50, 49) affected <- c(44, 42, 24, 16, 6, 0) finney71 <- data

What exactly does the LogisticRegression.predict_proba function return? In my example I get a result like this: [[ 4.65761066e-03 9.95342389e-01] [ 9.75851270e-01 2.41487300e-02] [ 9.99983374e-01 1.66258341e-05]] From other calculations, using the sigmoid function, I know, that the second column are probabilities. The documentation says, that the first column are n_samples , but that can't be, because my samples are reviews, which are texts and not numbers. The documentation also says, that the second column are n_classes . That certainly can't be, since I only have two classes (namely +1 and

Please help interpret results of logistic regression produced by weka.classifiers.functions.Logistic from Weka library. I use numeric data from Weka examples: @relation weather @attribute outlook {sunny, overcast, rainy} @attribute temperature real @attribute humidity real @attribute windy {TRUE, FALSE} @attribute play {yes, no} @data sunny,85,85,FALSE,no sunny,80,90,TRUE,no overcast,83,86,FALSE,yes rainy,70,96,FALSE,yes rainy,68,80,FALSE,yes rainy,65,70,TRUE,no overcast,64,65,TRUE,yes sunny,72,95,FALSE,no sunny,69,70,FALSE,yes rainy,75,80,FALSE,yes sunny,75,70,TRUE,yes overcast,72,90,TRUE,yes