logistic-regression

I'm using spark.mllib.classification.{LogisticRegressionModel, LogisticRegressionWithSGD} and spark.mllib.tree.RandomForest for classification. Using these packages I produce classification models. Only these models predict a specific class per instance. In Weka, we can get the exact probability for each instance to be of each class. How can we do it using these packages? In LogisticRegressionModel we can set the threshold. So I've created a function that check the results for each point on a different threshold. But this cannot be done for RandomForest (see How to set cutoff while training

I'm implementing logistic regression. I managed to get probabilities out of it, and am able to predict a 2 class classification task. My question is: For my final model, I have weights and the training data. There are 2 features, so my weight is a vector with 2 rows. How do I plot this? I saw this post , but I don't quite understand the answer. Do I need a contour plot? An advantage of the logistic regression classifier is that once you fit it, you can get probabilities for any sample vector. That may be more interesting to plot. Here's an example using scikit-learn: import numpy as np from

I am trying to use vowpal wabbit for logistic regression. I am not sure if this is the right syntax to do it For training, I do ./vw -d ~/Desktop/new_data.txt --passes 20 --binary --cache_file cache.txt -f lr.vw --loss_function logistic --l1 0.05 For testing I do ./vw -d ~/libsvm-3.18_test/matlab/new_data_test.txt --binary -t -i lr.vw -p predictions.txt -r raw_score.txt Here is a snippet from my train data -1:1.00038 | 110:0.30103 262:0.90309 689:1.20412 1103:0.477121 1286:1.5563 2663:0.30103 2667:0.30103 2715:4.63112 3012:0.30103 3113:8.38411 3119:4.62325 3382:1.07918 3666:1.20412 3728:5

I am trying to replicate a logit regression from Stata to R. In Stata I use the option "robust" to have the robust standard error (heteroscedasticity-consistent standard error). I am able to replicate the exactly same coefficients from Stata, but I am not able to have the same robust standard error with the package "sandwich". I have tried some OLS linear regression examples; it seems like the sandwich estimators of R and Stata give me the same robust standard error for OLS. Does anybody know how Stata calculate the sandwich estimator for non-linear regression, in my case the logit regression?

I ran a logistic regression model and made predictions of the logit values. I used this to get the points on the ROC curve: from sklearn import metrics fpr, tpr, thresholds = metrics.roc_curve(Y_test,p) I know metrics.roc_auc_score gives the area under the ROC curve. Can anyone tell me what command will find the optimal cut-off point (threshold value)? Manohar Swamynathan Though its late to answer, thought might be helpful. You can do this using the epi package in R (here!) , however I could not find similar package or example in python. The optimal cut off point would be where true positive