Tagged Questions

1
vote
1answer
47 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. ...
0
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0answers
94 views

Fitting a logistic regression using lassoglm in matlab

I am fitting a logistic regression model using lassoglm in matlab. I issued the following command ...
-1
votes
1answer
42 views

How does Logistic regression classifier modelize the dataset?

I'm working on a system that be able to detect the hand contour. So I have 270 instance in my dataset: 7 class of hand contour, 8 feature vectors of each instance. Firstly, I used Weka to determine ...
2
votes
1answer
140 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 ...
3
votes
0answers
85 views

Using priors to detect an effect? logistic Bayesian regression

I have designed an idea and am looking for similar approaches in other literature/areas or if I have applied the Bayesian concepts wrongly. Here is a statement of my problem: I am modeling the ...
1
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0answers
73 views

Explain ridge in the log-likelihood for Logistic Regression classifier

What does the ridge parameter change in a Logistic Regression classifier as for example implemented in Weka Logistic classifier "Parameter -R ridge". The paper describing the underlying theory: Ridge ...
3
votes
1answer
152 views

Logistic regression: how to choose negative examples for training set

I want to predict the probability of rain based on the measured weather parameters like temperature, humidity, etc. Let's not get into why I want to do that despite the fact that weather websites ...
2
votes
2answers
147 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
320 views

How to combine results of logistic regression and random forest?

I am new to machine learning. I applied logistic regression and random forest on a same dataset. So I get variable importance (absolute coefficient for logistic regression and variable importance for ...
6
votes
1answer
126 views

Logistic input with Gaussian noise

Both the logistic function and standard deviation are usually denoted $\sigma$. I'll use $\sigma(x) = 1/(1+\exp(-x))$ and $s$ for standard deviation. I have a logistic neuron with a random input ...
3
votes
2answers
234 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 ...
1
vote
1answer
109 views

Logistic Regression with weighted instances

I'm working on implementing a logistic regression algorithm in code. It's based this link. Unfortunately, the paper doesn't talk about weighting the individual examples $x_{i}$. I think the relevant ...
0
votes
1answer
277 views

Logistic Regression Algorithm?

I am studying on some Machine Learning concepts. I am looking for logistic regression(multiclass) and logistic regression classifier and I should learn how to change it to penalize large weights. I ...
0
votes
1answer
92 views

Algorithm convergence with logistic classifier

I am doing a college classification project, in which I am required to classify some handwritten digits. Assume that my input is a N*D where D is the number of features in each input sample and I need ...
0
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2answers
65 views

logistic regression always yielding increasing f'n when should sometimes be decreasing (using R)

I'm modeling a set of outcome data the depends on two parameters: time, T -100 < A < 100 I've done logistic regression using R with the command: ...
3
votes
0answers
92 views

Shifted intercepts in logistic regression

I have a question about the effects of shifting the intercept in a logistic fit on the mean of a particular transformation of the scores. Here is the notation I will be using for the question. The ...
1
vote
0answers
113 views

Standardize/normalize power law distribution for machine learning

If my data follows a normal distribution I can standardize it for a machine learning algorithm, e.g. logistic regression, by subtracting the mean and dividing the result by the standard deviation. ...
3
votes
1answer
149 views

Assumptions of GAM

I am looking to understand the assumptions of using a generalized additive model. 1) Are the assumptions the same as the assumptions for each equivalent link function in a generalized linear model - ...
0
votes
1answer
63 views

machine learning to predict equations / parameters of equations

I'm not sure if machine learning is the best way to do this, but I'm interested in seeing if this problem is feasible. Normally you use machine learning for classification. ie.given the size of a ...
2
votes
4answers
196 views

logistic regression. How to get dual function?

Given pairs $(x_i, y_i), x_i \in R^n , y_i \in R$ we want to solve minimization problem (logistic regression):$\min \frac{1}{2} ||w||^2 + \sum_i^{i=m}\log(1+\exp(-y w\cdot x_i))$. How to do that? I ...
4
votes
5answers
210 views

Performance metric for algorithm predicting probability of low probability events

I am writing a program to predict the click through rates of online ads. Two important notes about this problem: click through rates are very small (like 0.1%) click through rates depend on several ...
2
votes
2answers
570 views

Kernel logistic regression

I heard Kernel Logistic Regression is a classical combination of kernel methods and Logistic regression, but I cannot find any major reference (book, or paper) on this topic. Can you give me any ...
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. ...
3
votes
1answer
358 views

Solving for regression parameters in closed-form vs gradient descent

In Andrew Ng's machine learning course, he introduces linear regression and logistic regression, and shows how to fit the model parameters using gradient descent and Newton's method. I know gradient ...
5
votes
0answers
141 views

Updating classification probability in logistic regression through time

I am building a predictive model that forecasts a student's probability of success at the end of a term. I’m specifically interested in whether the student succeeds or fails, where success is usually ...
2
votes
1answer
51 views

Adjusting existing algorithm - likelihood for presence-only data

Logistic regression fits a model that predicts a binary variable whilst performing a logit transformation of the linear combination (LC) of predictors: 1/1 + exp(-LC). I have a working machine ...
4
votes
3answers
625 views

How do I handle predictor variables from different distributions in logistic regression?

I am using logistic regression to predict y given x1 and x2: z = B0 + B1 * x1 + B2 * x2 y = e^z / (e^z + 1) How is logistic regression supposed to handle cases ...
1
vote
1answer
122 views

Identifying culs-de-sac (circular housing arrangements) with GIS data

I'm thinking about a new project, so I don't have data yet, but I plan on collecting GIS information for houses within a state. Usually in the U.S., these dead-end streets will have a large circle ...
4
votes
2answers
237 views

GDA and LDA terminology

Can the terms LDA (Linear Discriminant Analysis) and GDA (Gaussian Discriminant Analysis) be used interchangeably? Do they often refer to the same thing?
2
votes
3answers
1k views

Logistic regression with LBFGS solver

Is there any open source library or code which implements Logistic Regression using L-BFGS solver? I would prefer Python, but other languages are welcome, too.
3
votes
1answer
462 views

What is the connection between Kernel Logistic Regression and Smoothing Splines?

Working on probabilistic outputs of kernel methods I found the formulation of the SVM as a Penalized Method using the Binomial Deviance (described for example in "The Elements of Statistical Learning ...