glm

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

We frequently score data in database directly for simple models like linear or logisitc regression. It is always a little bit tricky to transfer all coefficients from R to SQL correctly. I thought I can make some R to SQL translation for glm result. For numeric variables this is pretty straightforward: library(rpart) fit <- glm(Kyphosis ~ ., data = kyphosis, family = binomial()) coefs <- fit$coef[2:length(fit$coef)] expr <- paste0('1/(1 + exp(-(',fit$coef[1], '+', paste0('(', coefs, '*', names(coefs), ')', collapse = '+'),')))') print(expr) a <- with(kyphosis, eval(parse(text = expr))) b <-

I'm interested in creating interaction terms in h2o.glm(). But I do not want to generate all pairwise interactions. For example, in the mtcars dataset...I want to interact 'mpg' with all the other factors such as 'cyl','hp', and 'disp' but I don't want the other factors to interact with each other (so I don't want disp_hp or disp_cyl). How should I best approach this problem using the (interactions = interactions_list) parameter in h2o.glm() ? Thank you According to ?h2o.glm the interactions= parameter takes: A list of predictor column indices to interact. All pairwise combinations will be

I'm trying to do some glm's inside a data.table to produce modelled results split by key factors. I've been doing this sucessfully for: High level glm glm(modellingDF,formula=Outcome~IntCol + DecCol,family=binomial(link=logit)) Scoped glm with single columns modellingDF[,list(Outcome, fitted=glm(x,formula=Outcome~IntCol ,family=binomial(link=logit))$fitted ), by=variable] Scoped glm with two integer columns modellingDF[,list(Outcome, fitted=glm(x,formula=Outcome~IntCol + IntCol2 ,family=binomial(link=logit))$fitted ), by=variable] But, when I try and do the high level glm inside the scope with

I am trying to model my data in which the response variable is between 0 and 1, so I have decided to use fractional response model in R. From my current understanding, the fractional response model is similar to logistic regression, but it uses qausi-likelihood method to determine parameters. I am not sure I understand it correctly. So far what I have tried is the frm from package frm and glm on the following data, which is the same as this OP library(foreign) mydata <- read.dta("k401.dta") Further, I followed the procedures in this OP in which glm is used. However, with the same dataset with

I'm looking for a way to specify the value of a predictor variable. When I run a glm with my current data, the coefficient for one of my variables is close to one. I'd like to set it at .8. I know this will give me a lower R^2 value, but I know a priori that the predictive power of the model will be greater. The weights component of glm looks promising, but I haven't figured it out yet. Any help would be greatly appreciated. I believe you are looking for the offset argument in glm . So for example, you might do something like this: glm(y ~ x1, offset = x2,...) where in this case the