Tagged Questions

0
votes
1answer
82 views

How to handle Regression data thats not linear

I'm new to stats and am using Python 2.7 to fit a regression model (Random Forest). When I plot the percentile plot of the prices before and after a log ...
0
votes
0answers
44 views

When to Log/Exp your Variables when performing Linear Regression?

I'm doing regression using Random Forests for predicting prices based on several attributes. Code is written in Python using Scikit-learn. How do you decide whether you should transform your ...
0
votes
1answer
64 views

Why is Hedonic Regression used instead of Linear Regression

Why is Hedonic Regression used (especially in housing prices) instead of Linear Regression? There do not seem to be any libraries in Python (and R) for Hedonic regression, is it too niched a ...
3
votes
0answers
38 views

Link Anomaly Detection in Temporal Network

I came across this paper that uses link anomaly detection to predict trending topics, and I found it incredibly intriguing: The paper is "Discovering Emerging Topics in Social Streams via Link Anomaly ...
2
votes
0answers
46 views

How should I distribute a classifier to customers?

When consulting, I often do my exploratory analysis and prototyping in R, and deliver results on the initial dataset to the client. The client wants to use the trained classifier in a production ...
0
votes
0answers
41 views

Weighting and time series with machine learning

I'm trying to produce a model to predict the price of a product on the basis of several factors effecting previous time-stamped sales. I am certain that older sales are less relevant to the prediction ...
2
votes
0answers
72 views

detecting circadian rhythm in a time series

I have a sensor that can detect minute changes in distance. It produces a time series. I would like to point it at people and detect things like their sleeping pattern. How would one build a system ...
2
votes
1answer
164 views

Simple text classifier: classification taking forever?

I work for a small tech startup, and I want to classify or users into demographics based on the domain of their email address. When users sign up to our site, they can enter a job category, or pick ...
2
votes
1answer
123 views

Simple machine learning: bot detection

I've been aching to get my feet wet with a machine learning project, and I've found one that should be relatively simple, and actually has non-negligible business value for my organization. The ...
5
votes
2answers
723 views

Pandas / Statsmodel / Scikits-learn

Are Pandas, Statsmodels and scikits-learn different implementations of machine learning/statistical operations, or are these complementary to one another? Which of these has the most comprehensive ...
0
votes
0answers
80 views

Mixing categorial and continuous data in Naive Bayes classifier using scikit-learn

I'm using scikit-learn in Python to develop a classification algorithm to predict gender of a certain customers. Amongst others I want to use the Naive Bayes classifier but my problem is that I have a ...
6
votes
1answer
1k views

R vs Python for Data Analysis [duplicate]

Possible Duplicate: Python as a statistics workbench I am just starting out with data analysis and machine learning. From the books that I am reading/have read Python and R seem to be the ...
3
votes
1answer
105 views

Practical applications of affinity propagation

I am learning about machine learning here. They took a set of prices for specific companies on the stock market and graphed them: I would like to know what are some practical applications of ...
2
votes
2answers
401 views

Implementation of CRF in python

Is there a popular implementation of Conditional Random Fields in Python? I can't seem to find any that is widely used and popular!
0
votes
0answers
167 views

Best platform for running (python and ( R or Octave)) algorithms for (large/big) data analytics [closed]

I have a machine learning algorithm currently implemented in R, wrapped in python (rpy2). I would like to deploy this inside a web application and I am looking for the right platform to do this, ...
4
votes
3answers
601 views

Resources for learning how to implement ensemble methods

I understand theoretically (sort of) how they would work, but am not sure how to go about actually making use an ensemble method (such as voting, weighted mixtures, etc.). What are good resources ...
8
votes
3answers
188 views

What is the most efficient way of training data using least memory?

This is my training data: 200,000 Examples x 10,000 Features. So my training data matrix is - 200,000 x 10,000. I managed to save this in a flat file without having memory issues by saving every ...
0
votes
2answers
275 views

Collinear variables in Multiclass LDA training

I'm training a Multi-class LDA classifier with 8 classes of data. While performing training, I get a warning of: "Variables are collinear" I'm getting a training accuracy of over 90%. I'm using ...
0
votes
0answers
221 views

Multi Class vs 2 class Naive Bayes

I was wondering what are the implications of using a multi-class Naive Bayes versus a 2 class Naive Bayes (for one against everything). Which technique performs better? I've previously came across ...
3
votes
2answers
360 views

Using Python for building machine learning application

I'm currently using R to find the best approach to solving a machine learning problem. Once I've got the approach sorted, I will need to build this into an application which can be used by end users. ...
0
votes
1answer
190 views

SVM options in scikit-learn

Just curious about two options in the scikits SVM class. Does anyone know what Scale C and shrinking do? Not much in the documentation unfortunately. Thanks
5
votes
1answer
500 views

TF-IDF cutoff percentage for tweets

I'm currently trying to analyze Tweets and classify them as either positive, negative, or neutral using the NLTK library in Python. I can see that there's potential in the approach that I'm taking, ...
5
votes
2answers
814 views

Are there any tutorials on bayesian probability theory or graphical models by example?

I've seen references to learning Bayesian probability theory in R, and I was wondering if there is more like this, perhaps specifically in Python? Geared towards learning Bayesian probability theory, ...
8
votes
4answers
1k views

What programming language do you recommend to prototype a machine learning problem?

Currently working in Octave, but due to the poor documentation progress is very slow. What language is easy to learn and use, and well documented to solve machine learning problems? I am looking to ...
6
votes
1answer
262 views

Gibbs sampling for a simple linear model — need help with the likelihood function

So in order to better acquaint myself with Gibbs sampling, I've been working on a fairly simple linear model, written in Python/R. Basically, I have 2-dimensional input data (the xi) and a scalar ...
31
votes
8answers
6k views

Machine Learning using Python

I am considering using Python libraries for doing my Machine Learning experiments. Thus far, I had been relying on WEKA but have been pretty dissatisfied on the whole. This is primarily because I have ...