logistic-regression

I'm working through my Matlab code for the Andrew NG Coursera course and turning it into python. I am working on non-regularized logistic regression and after writing my gradient and cost functions I needed something similar to fminunc and after some googling, I found a couple options. They are both returning the same results, but they do not match what is in Andrew NG's expected results code. Others seem to be getting this to work correctly, but I'm wondering why my specific code does not seem to return the desired result when using scipy.optimize functions, but does for the cost and gradient

I get this error when trying to fit glmnet() with family="binomial", for Logistic Regression fit: > data <- read.csv("DAFMM_HE16_matrix.csv", header=F) > x <- as.data.frame(data[,1:3]) > x <- model.matrix(~.,data=x) > y <- data[,4] > train=sample(1:dim(x)[1],287,replace=FALSE) > xTrain=x[train,] > xTest=x[-train,] > yTrain=y[train] > yTest=y[-train] > fit = glmnet(xTrain,yTrain,family="binomial") Error in lognet(x, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one multinomial or binomial class has 1 or 0 observations; not allowed Any help would be greatly appreciated - I've searched

I use a same dataset to train logistic regression model both in R and python sklearn. The dataset is unbalanced. And I find that the auc is quite different. This is the code of python: model_logistic = linear_model.LogisticRegression() #auc 0.623 model_logistic.fit(train_x, train_y) pred_logistic = model_logistic.predict(test_x) #mean:0.0235 var:0.023 print "logistic auc: ", sklearn.metrics.roc_auc_score(test_y,pred_logistic) This is the code of R: glm_fit <- glm(label ~ watch_cnt_7 + bid_cnt_7 + vi_cnt_itm_1 + ITEM_PRICE + add_to_cart_cnt_7 + offer_cnt_7 + dwell_dlta_4to2 + vi_cnt_itm_2 + asq

I created a zero inflated negative binomial model and want to investigate how many of the zeros were partitioned out to sampling or structural zeros. How do I implement this in R. The example code on the zeroinfl page is not clear to me. data("bioChemists", package = "pscl") fm_zinb2 <- zeroinfl(art ~ . | ., data = bioChemists, dist = "negbin") table(round(predict(fm_zinb2, type="zero"))) > 0 1 > 891 24 table(round(bioChemists$art)) > 0 1 2 3 4 5 6 7 8 9 10 11 12 16 19 > 275 246 178 84 67 27 17 12 1 2 1 1 2 1 1 What is this telling me? When I do the same for my data I get a read out that just

I want to reproduce the following drc::plot.drc graphs with ggplot2 . df1 <- structure(list(TempV = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 10L, 10L, 10L, 10L, 10L, 10L

I am performing logistic regression using Vowpal Wabbit on a dataset with 25 features and 48 million instances. I have a question on current predict values. Should it be within 0 or 1. average since example example current current current loss last counter weight label predict features 0.693147 0.693147 1 1.0 -1.0000 0.0000 24 0.419189 0.145231 2 2.0 -1.0000 -1.8559 24 0.235457 0.051725 4 4.0 -1.0000 -2.7588 23 6.371911 12.508365 8 8.0 -1.0000 -3.7784 24 3.485084 0.598258 16 16.0 -1.0000 -2.2767 24 1.765249 0.045413 32 32.0 -1.0000 -2.8924 24 1.017911 0.270573 64 64.0 -1.0000 -3.0438 25 0