logistic-regression

Step 0: Problem description I have a classification problem, ie I want to predict a binary target based on a collection of numerical features, using logistic regression, and after running a Principal Components Analysis (PCA). I have 2 datasets: df_train and df_valid (training set and validation set respectively) as pandas data frame, containing the features and the target. As a first step, I have used get_dummies pandas function to transform all the categorical variables as boolean. For example, I would have: n_train = 10 np.random.seed(0) df_train = pd.DataFrame({"f1":np.random.random(n

I am trying to predict a set of labels using Logistic Regression from SciKit. My data is really imbalanced (there are many more '0' than '1' labels) so I have to use the F1 score metric during the cross-validation step to "balance" the result. [Input] X_training, y_training, X_test, y_test = generate_datasets(df_X, df_y, 0.6) logistic = LogisticRegressionCV( Cs=50, cv=4, penalty='l2', fit_intercept=True, scoring='f1' ) logistic.fit(X_training, y_training) print('Predicted: %s' % str(logistic.predict(X_test))) print('F1-score: %f'% f1_score(y_test, logistic.predict(X_test))) print('Accuracy

I have a test dataset and train dataset as below. I have provided a sample data with min records, but my data has than 1000's of records. Here E is my target variable which I need to predict using an algorithm. It has only four categories like 1,2,3,4. It can take only any of these values. Training Dataset: A B C D E 1 20 30 1 1 2 22 12 33 2 3 45 65 77 3 12 43 55 65 4 11 25 30 1 1 22 23 19 31 2 31 41 11 70 3 1 48 23 60 4 Test Dataset: A B C D E 11 21 12 11 1 2 3 4 5 6 7 8 99 87 65 34 11 21 24 12 Since E has only 4 categories, I thought of predicting this using Multinomial Logistic Regression

I got a weird result from vw , which uses online learning scheme for logistic regression. And when I add --l1 or --l2 regularization then I got all predictions at 0.5 (that means all features are 0) Here's my command: vw -d training_data.txt --loss_function logistic -f model_l1 --invert_hash model_readable_l1 --l1 0.05 --link logistic ...and here's learning process info: using l1 regularization = 0.05 final_regressor = model_l1 Num weight bits = 18 learning rate = 0.5 initial_t = 0 power_t = 0.5 using no cache Reading datafile = training_data.txt num sources = 1 average since example example

I have a logistic regression model (using R) as fit6 <- glm(formula = survived ~ ascore + gini + failed, data=records, family = binomial) summary(fit6) I'm using pROC package to draw ROC curves and figure out AUC for 6 models fit1 through fit6. I have approached this way to plots one ROC. prob6=predict(fit6,type=c("response")) records$prob6 = prob6 g6 <- roc(survived~prob6, data=records) plot(g6) But is there a way I can combine the ROCs for all 6 curves in one plot and display the AUCs for all of them, and if possible the Confidence Intervals too. You can use the add = TRUE argument the plot