Tagged Questions

1
vote
1answer
50 views

Online logistic regression?

Here is my problem: I am developing an embedded system for some classification task. I am using Logistic Regression as my classifier. Now I train my classifier, and download my model on to my machine. ...
1
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1answer
59 views

What method should I use to identify which variables differentiate between objects of two different classes?

To illustrate the problem posed by the question: Consider the problem of differentiating between consumers who belong to two different segments. I could use a naive or a sophisticated approach as ...
0
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0answers
26 views

How do you generate synthetic sparse binary linguistic data for logistic regression?

I am trying to generate synthetic linguistic data (boolean features) to fit a binary logistic regression model. This is similar to 8260771 on StackOverflow and several synthetic data questions on this ...
2
votes
1answer
141 views

Logistic regression as classifier and overfitting

I am using logistic regression to classify data into two classes. The variable to predict (Y) is either 0 or 1. I have found a rather good estimation of Y by logistic regression, and ended up using ...
0
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0answers
40 views

Reformulate binary classification: Relative penalty of false positives versus false negatives

I have a training data for a set of insurance claims that I used to train multiple models within R (1. Binomial Logistic regression 2. Naive Bayes 3. k nearest neighbor algorithm) The binomial ...
2
votes
2answers
148 views

Importance of variables in logistic regression

I am probably dealing with a problem that has probably been solved a hundred times before, but I'm not sure where to find the answer. When using logistic regression, given many features $x_1,...,x_n$ ...
3
votes
2answers
235 views

SVM prediction sensitivity when compared to neural networks and logistic regression

Basically I want to classify a rather rare status (about 2% of the 2000) with some predictors. I have used logistic regression, neural network, and Support Vector Machines to do it. All the ...
0
votes
0answers
96 views

Algorithm definition and steps of logistic regression for multiple classes

I am studying logistic regression classifiers for multiple classes and I could only find theoretical explanations of it. I have data like this: ...
0
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1answer
134 views

Does learning rate have additional meaning in logistic regression?

I try to implement logistic regression with auto-correcting learning rate and I am puzzled by the outcome. At some point the cost of the function gets bigger than previously (to focus on some numbers ...
2
votes
1answer
94 views

Accuracy vs. simplicity as criteria to select explanatory variables

I want to create a binary model which predicts whether someone has improved his state. I am testing possible variables as explanatory variables in order to make some recommendations. Now my binary ...
2
votes
2answers
622 views

Interpreting logistic regression

I need to perform a logistic regression to to see if a group of variables which are found to be significantly associated with an outcome (by univariate tests) have significant impact on the outcome ...
3
votes
2answers
158 views

Multilabel logistic regression

Is there a way to use logistic regression to classify multi-labeled data? By multi-labeled, I mean data that can belong to multiple categories simultaneously. I would like to use this approach to ...
0
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0answers
608 views

How to combine WEKA classifiers

I need to utilize two different classifier to get best classification results. Since, it seems that they complement each other (not sure I am not expert btw). ROC characteristics are given below ...
0
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1answer
173 views

What are the advantages of different classification algorithms? [closed]

For example, when should one use the decision trees over logistic regression (or vice versa) for classification?
2
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3answers
771 views

Logistic regression performance with high number of predictors

I'm trying to understand the behavior of logistic regression in high dimensional problems (i.e. when you are fitting a logistic regression to data with a high number of predictor variables). Every ...
0
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1answer
129 views

Classification trees to pick predictive variables

Running a logistic regression we get p-values for all the input variables which helps us choose significant inputs. Similarly can we use the classification trees to pick variables that are split, and ...
3
votes
2answers
206 views

Logistic regression is slow

I am relatively new to machine learning and I have a data classification problem where each sample has ~1500 features (continuous) and the category is binary. I want to apply logistic regression, for ...
2
votes
1answer
931 views

Logistic regression on categorical data

I have large dataset (around 2 million records and 300 features) with a lot of missing data. Most of the independent variables are categorical (some of these variables have more than 40 valid values). ...
0
votes
1answer
124 views

Are there popular dimensionality reduction tools for classification type supervised learning?

I am familiar with PCA as a linear transformation in order to align the axis of the IV space with the directions of maximal variance in order to possibly be able to reduce the dimensionality of the ...
1
vote
1answer
101 views

Remove duplicates from training set for classification

Let us say I have a bunch of rows for a classification problem: $$X_1, ... X_N, Y$$ Where $X_1, ..., X_N$ are the features/predictors and $Y$ is the class the row’s feature combination belongs to. ...
1
vote
1answer
707 views

How to interpret result for MultiModelByRegression in RapidMiner?

I couldn't find any information in the documentation of rapidminer. I have a data set with the following attributes: a,b,c,d,e. The types are: ...
4
votes
3answers
179 views

Options for comparing logistic regression models

I want to choose variables for building a logistic regression model by comparing the GOF of different types. The problem is that some of the candidates have some missing values so I can´t use the ...
2
votes
1answer
140 views

Logical extensions after logistic regression

I have a number of predictors to use for a binary (Classes 0 and 1) classification task. Let us call them $x_1, x_2, x_3, ... x_n$. The way these are calculated, my naive heuristic assumption is that ...
4
votes
1answer
96 views

Constrain decision boundary to fall on grid lines in multiple class logistic regression

I would like to use multiple class logistic regression to learn the decision boundaries separating the different classes (denoted by color) in the image below. Kernel logistic regression with a RBF ...
0
votes
1answer
409 views

Comparing logistic regression models

I have a 2(truthfulness) x 2 (immediate test) study where I am looking to compare 2 logistic regression models. Both are classifying the same outcome variable: truthfulness (truthful versus ...
1
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2answers
491 views

Are Fisher Linear Discriminant and Logistic Regression Classifier related?

I have some experience with both FLD and LR for classification. On most data sets, I get very similar results, which raises the question - are FLD and LR related in some why? An idea, for example, ...
1
vote
1answer
218 views

Logistic regression with non-negative parameter

I want to model the probability of a binary variable x given some predictor, d. It needs two parameters: One parameter that sets the "break point", at which p(x=1 | d) = 0.5. One parameter that ...
6
votes
1answer
183 views

Coefficient / model averaging to control for exogenous circumstances in prediction

I'm interested in exploring statistical models (or modifications thereof) designed to handle a specific type of problem. Due to my ignorance of statistical terminology, I can only describe this type ...
26
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5answers
2k views

Alternatives to logistic regression in R

I would like as many algorithms that perform the same task as logistic regression. That is algorithms/models that can give a prediction to a binary response (Y) with some explanatory variable (X). ...