logistic-regression

I have a training dataset of 8670 trials and each trial has a length of 125-time samples while my test set consists of 578 trials. When I apply SVM algorithm from scikit-learn, I get pretty good results. However, when I apply logistic regression, this error occurs: "ValueError: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1.0" . My question is why SVM is able to give predictions but logistic regression gives this error? Could it be possible that something is wrong in the dataset or just that logistic regression was not able to classify

I am trying to use spark for some simple machine learning task. I used pyspark and spark 1.2.0 to do a simple logistic regression problem. I have 1.2 million records for training, and I hashed the features of the records. When I set the number of hashed features as 1024, the program works fine, but when I set the number of hashed features as 16384, the program fails several times with the following error: Py4JJavaError: An error occurred while calling o84.trainLogisticRegressionModelWithSGD. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 4.0 failed 4 times

I am trying to develop stochastic gradient descent, but I don't know if it is 100% correct. The cost generated by my stochastic gradient descent algorithm is sometimes very far from the one generated by FMINUC or Batch gradient descent. while batch gradient descent cost converge when I set a learning rate alpha of 0.2, I am forced to set a learning rate alpha of 0.0001 for my stochastic implementation for it not to diverge. Is this normal? Here are some results I obtained with a training set of 10,000 elements and num_iter = 100 or 500 FMINUC : Iteration #100 | Cost: 5.147056e-001 BACTH

I am going nuts trying to figure this out. How can I in R, define the reference level to use in a binary logistic regression? What about the multinomial logistic regression? Right now my code is: logistic.train.model3 <- glm(class~ x+y+z, family=binomial(link=logit), data=auth, na.action = na.exclude) my response variable is "YES" and "NO". I want to predict the probability of someone responding with "YES". I DO NOT want to recode the variable to 0 / 1. Is there a way I can tell the model to predict "YES" ? Thank you for your help. smrt1119 Assuming you have class saved as a factor, use the

I would like to use cross validation to test/train my dataset and evaluate the performance of the logistic regression model on the entire dataset and not only on the test set (e.g. 25%). These concepts are totally new to me and am not very sure if am doing it right. I would be grateful if anyone could advise me on the right steps to take where I have gone wrong. Part of my code is shown below. Also, how can I plot ROCs for "y2" and "y3" on the same graph with the current one? Thank you import pandas as pd Data=pd.read_csv ('C:\\Dataset.csv',index_col='SNo') feature_cols=['A','B','C','D','E'] X

I need to know how to return the logistic regression coefficients in such a manner that I can generate the predicted probabilities myself. My code looks like this: lr = LogisticRegression() lr.fit(training_data, binary_labels) # Generate probabities automatically predicted_probs = lr.predict_proba(binary_labels) I had assumed the lr.coeff_ values would follow typical logistic regression, so that I could return the predicted probabilities like this: sigmoid( dot([val1, val2, offset], lr.coef_.T) ) But this is not the appropriate formulation. Does anyone have the proper format for generating