logistic-regression

I have a model built with Python scikit-learn. I understand that the models can be saved in Pickle or Joblib formats. Are there any existing methods out there to save the jobs in JSON format? Please see the model build code below for reference: import pandas from sklearn import model_selection from sklearn.linear_model import LogisticRegression import pickle url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data" names =['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] dataframe = pandas.read_csv(url, names=names

I am in the beginning stages of machine learning in R and I find it hard to believe that there are no packages to solving the cost function for different types of regression algorithms. For example, if I want to solve the cost function for a logistic regression, the manual way would be below: https://www.r-bloggers.com/logistic-regression-with-r-step-by-step-implementation-part-2/ # Implement Sigmoid function sigmoid <- function(z) { g <- 1/(1+exp(-z)) return(g) } #Cost Function cost <- function(theta) { m <- nrow(X) g <- sigmoid(X%*%theta) J <- (1/m)*sum((-Y*log(g)) - ((1-Y)*log(1-g))) return

I am currently working on a machine learning project where - given a data matrix Z and a vector rho - I have to compute the value and slope of the logistic loss function at rho . The computation involves basic matrix-vector multiplication and log/exp operations, with a trick to avoid numerical overflow (described in this previous post ). I am currently doing this in Python using NumPy as shown below (as a reference, this code runs in 0.2s). Although this works well, I would like to speed it up since I call the function multiple times in my code (and it represents over 90% of the computation

I performed Logistic regression on a binary classification problem with data of 50000 X 370 dimensions.I got accuracy of about 90%.But when i did PCA + logistic on data, my accuracy reduced to 10%, I was very shocked to see this result. Can anybody explain what could have gone wrong? There is no guarantee that PCA will ever help, or not harm the learning process. In particular - if you use PCA to reduce amount of dimensions - you are removing information from your data, thus everything can happen - if the removed data was redundant, you will probably get better scores, if it was an important

I've fitted a logistic regression model that predicts the a binary outcome vs from mpg ( mtcars dataset). The plot is shown below. How can I determine the mpg value for any particular vs value? For example, I'm interested in finding out what the mpg value is when the probability of vs is 0.50. Appreciate any help anyone can provide! model <- glm(vs ~ mpg, data = mtcars, family = binomial) ggplot(mtcars, aes(mpg, vs)) + geom_point() + stat_smooth(method = "glm", method.args = list(family = "binomial"), se = FALSE) The easiest way to calculate predicted values from your model is with the predict

I'm a bit new to r and I would like to use a package that allows multi cores processing in order to run glm function faster.I wonder If there is a syntax that I can use for this matter. Here is an example glm model that I wrote, can I add a parameter that will use multi cores ? g<-glm(IsChurn~.,data=dat,family='binomial') Thanks. Other usefull packages are: http://cran.r-project.org/web/packages/gputools/gputools.pdf with gpuGlm and http://cran.r-project.org/web/packages/mgcv/mgcv.pdf see mgcv.parallel section about gam(..., control=list(nthreads=nc)) or bam(..., cluster=makeCluster(nc)) where