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Questions tagged [machine-learning]

Machine Learning is a subfield of computer science that draws on elements from algorithmic analysis, computational statistics, mathematics, optimization, etc. It is mainly concerned with the use of data to construct models that have high predictive/forecasting ability. Topics include modeling building, applications, theory, etc.

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4 answers

Suggest text classifier training datasets

Which freely available datasets can I use to train a text classifier? We are trying to enhance our users engagement by recommending the most related content for him, so we thought If we classified ...
Abdelmawla's user avatar
16 votes
4 answers

What are the implications for training a Tree Ensemble with highly biased datasets?

I have a highly biased binary dataset - I have 1000x more examples of the negative class than the positive class. I would like to train a Tree Ensemble (like Extra Random Trees or a Random Forest) on ...
gallamine's user avatar
  • 418
9 votes
1 answer

Learning signal encoding

I have a large number of samples which represent Manchester encoded bit streams as audio signals. The frequency at which they are encoded is the primary frequency component when it is high, and there ...
ragingSloth's user avatar
  • 1,824
14 votes
4 answers

Looking for example infrastructure stacks/workflows/pipelines

I'm trying to understand how all the "big data" components play together in a real world use case, e.g. hadoop, monogodb/nosql, storm, kafka, ... I know that this is quite a wide range of tools used ...
chrshmmmr's user avatar
  • 143
10 votes
4 answers

Online machine learning tutorial

Does anyone know some good tutorials on online machine learning technics? I.e. how it can be used in real-time environments, what are key differences compared to normal machine learning methods etc. ...
Igor Bobriakov's user avatar
16 votes
4 answers

Best way to classify datasets with mixed types of attributes

I would like to know what is the best way to classify a data set composed of mixed types of attributes, for example, textual and numerical. I know I can convert textual to boolean, but the vocabulary ...
user900's user avatar
  • 161
114 votes
11 answers

Choosing a learning rate

I'm currently working on implementing Stochastic Gradient Descent, SGD, for neural nets using back-propagation, and while I understand its purpose I have some ...
ragingSloth's user avatar
  • 1,824
21 votes
7 answers

How can I predict traffic based on previous time series data?

If I have a retail store and have a way to measure how many people enter my store every minute, and timestamp that data, how can I predict future foot traffic? I have looked into machine learning ...
user1132959's user avatar
9 votes
2 answers

Difference between using RMSE and nDCG to evaluate Recommender Systems

What kind of error measures do RMSE and nDCG give while evaluating a recommender system, and how do I know when to use one over the other? If you could give an example of when to use each, that would ...
covfefe's user avatar
  • 293
127 votes
14 answers

Python vs R for machine learning

I'm just starting to develop a machine learning application for academic purposes. I'm currently using R and training myself in it. However, in a lot of places, I have seen people using Python. What ...
6 votes
1 answer

How can we calculate AUC for a simple decision tree?

The setup is simple: binary classification using a simple decision tree, each node of the tree has a single threshold applied on a single feature. In general, building a ROC curve requires moving a ...
iliasfl's user avatar
  • 609
10 votes
2 answers

Debugging Neural Networks

I've built an artificial neural network in python using the scipy.optimize.minimize (Conjugate gradient) optimization function. I've implemented gradient checking, double checked everything etc and I'...
user3726050's user avatar
17 votes
3 answers

One-Class discriminatory classification with imbalanced, heterogenous Negative background?

I'm working on improving an existing supervised classifier, for classifying {protein} sequences as belonging to a specific class (Neuropeptide hormone precursors), or not. There are about 1,150 known ...
GrimSqueaker's user avatar
12 votes
9 answers

What are some easy to learn machine-learning applications? [closed]

Being new to machine-learning in general, I'd like to start playing around and see what the possibilities are. I'm curious as to what applications you might recommend that would offer the fastest ...
Steve Kallestad's user avatar
42 votes
10 answers

Can machine learning algorithms predict sports scores or plays?

I have a variety of NFL datasets that I think might make a good side-project, but I haven't done anything with them just yet. Coming to this site made me think of machine learning algorithms and I ...
Steve Kallestad's user avatar
16 votes
2 answers

Where in the workflow should we deal with missing data?

I'm building a workflow for creating machine learning models (in my case, using Python's pandas and sklearn packages) from data ...
Therriault's user avatar
9 votes
3 answers

Human activity recognition using smartphone data set problem

I'm new to this community and hopefully my question will well fit in here. As part of my undergraduate data analytics course I have choose to do the project on human activity recognition using ...
Jakubee's user avatar
  • 401
13 votes
4 answers

Algorithm for generating classification rules

So we have potential for a machine learning application that fits fairly neatly into the traditional problem domain solved by classifiers, i.e., we have a set of attributes describing an item and a "...
super_seabass's user avatar
15 votes
4 answers

How to specify important attributes?

Assume a set of loosely structured data (e.g. Web tables/Linked Open Data), composed of many data sources. There is no common schema followed by the data and each source can use synonym attributes to ...
vefthym's user avatar
  • 503
6 votes
1 answer

Is Data Science just a trend or is a long term concept? [closed]

I see a lot of courses in Data Science emerging in the last 2 years. Even big universities like Stanford and Columbia offers MS specifically in Data Science. But as long as I see, it looks like data ...
Filipe Ferminiano's user avatar
28 votes
6 answers

Machine learning techniques for estimating users' age based on Facebook sites they like

I have a database from my Facebook application and I am trying to use machine learning to estimate users' age based on what Facebook sites they like. There are three crucial characteristics of my ...
Wojciech Walczak's user avatar
15 votes
2 answers

Is there any APIs for crawling abstract of paper?

If I have a very long list of paper names, how could I get abstract of these papers from internet or any database? The paper names are like "Assessment of Utility in Web Mining for the Domain of ...
Alex Gao's user avatar
  • 253
57 votes
8 answers

Why Is Overfitting Bad in Machine Learning?

Logic often states that by overfitting a model, its capacity to generalize is limited, though this might only mean that overfitting stops a model from improving after a certain complexity. Does ...
blunders's user avatar
  • 1,932
17 votes
2 answers

Use liblinear on big data for semantic analysis

I use Libsvm to train data and predict classification on semantic analysis problem. But it has a performance issue on large-scale data, because semantic analysis concerns n-dimension problem. Last ...
Puffin GDI's user avatar
9 votes
1 answer

How can I do simple machine learning without hard-coding behavior? [closed]

I've always been interested in machine learning, but I can't figure out one thing about starting out with a simple "Hello World" example - how can I avoid hard-coding behavior? For example, if I ...
Doorknob's user avatar
  • 215

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