Methods and principles of building "computer systems that automatically improve with experience."

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173
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17answers
41k views

The Two Cultures: statistics vs. machine learning?

Last year, I read a blog post from Brendan O'Connor entitled "Statistics vs. Machine Learning, fight!" that discussed some of the differences between the two fields. Andrew Gelman responded favorably ...
39
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12answers
6k views

Machine learning cookbook / reference card / cheatsheet?

I find resources like the Probability and Statistics Cookbook and The R Reference Card for Data Mining incredibly useful. They obviously serve well as references but also help me to organize my ...
83
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12answers
51k views

What is the difference between data mining, statistics, machine learning and AI?

What is the difference between data mining, statistics, machine learning and AI? Would it be accurate to say that they are 4 fields attempting to solve very similar problems but with different ...
31
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8answers
5k views

Feature selection for “final” model when performing cross-validation in machine learning

I am getting a bit confused about feature selection and machine learning and I was wondering if you could help me out. I have a microarray dataset that is classified into two groups and has 1000s of ...
51
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1answer
2k views

Help me understand Support Vector Machines

I understand the basics of what a Support Vector Machines' aim is in terms of classifying an input set into several different classes, but what I don't understand is some of the nitty-gritty details. ...
63
votes
7answers
42k views

What is the difference between test set and validation set?

I found this confusing when I use the neural network toolbox in Matlab. It divided the raw data set into three parts: training set validation set test set I notice in many training or learning ...
38
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5answers
7k views

Training with the full dataset after cross-validation?

Is it always a good idea to train with the full dataset after k-fold cross-validation? Or is it better instead to stick with one of the models learned in one of the cross-validation splits for ...
71
votes
5answers
8k views

What skills are required to perform large scale statistical analyses?

Many statistical jobs ask for experience with large scale data. What are the sorts of statistical and computational skills that would be need for working with large data sets. For example, how about ...
6
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1answer
1k views

PCA and the train/test split

I have a dataset for which I have multiple sets of binary labels. For each set of labels, I train a classifier, evaluating it by cross-validation. I want to reduce dimensionality using principal ...
23
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3answers
2k views

Understanding Naive Bayes

To demonstrate the concept of Naïve Bayes Classification, consider the example displayed in the illustration above. As indicated, the objects can be classified as either GREEN or RED. My task is to ...
30
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2answers
8k views

Choice of K in K-Fold cross validation

I've been using the K-Fold cross validation a few times now to evaluate performance of some learning algorithms, but I've always been puzzled as to how I should choose the value of K. I've often seen ...
23
votes
2answers
5k views

Proper way of using recurrent neural network for time series analysis

Recurrent neural networks differ from "regular" ones by the fact that they have a "memory" layer. Due to this layer, recurrent NN's are supposed to be useful in time series modelling. However, I'm not ...
9
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3answers
3k views

Comparing clusterings: Rand Index vs Variation of Information

I was wondering if anybody had any insight or intuition behind the difference between the Variation of Information and the Rand Index for comparing clusterings. I have read the paper "Comparing ...
10
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0answers
1k views

Machine learning self-learning book? [duplicate]

Possible Duplicate: Machine learning cookbook / reference card / cheatsheet? I wonder if there is a good self-learning textbook for machine learning? I am particularly looking for those in ...
23
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6answers
5k views

Can you recommend a book to read before Elements of Statistical Learning?

Based on this post, http://quant.stackexchange.com/questions/111/how-can-i-go-about-applying-machine-learning-algorithms-to-stock-markets, I want to digest Elements of Statistical Learning. ...
11
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2answers
2k views

How does linear discriminant analysis reduce the dimensions?

There are words from "The Elements of Statistical Learning" on page 91: The K centroids in p-dimensional input space span at most K-1 dimensional subspace, and if p is much larger than K, this ...
48
votes
10answers
23k views

Having a job in data-mining without a PhD

I've been very interested in data-mining and machine-learning for a while, partly because I majored in that area at school, but also because I am truly much more excited trying to solve problems that ...
32
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4answers
10k views

How to select kernel for SVM?

When using SVM, we need to select a kernel. I wonder how to select a kernel. Any criteria on kernel selection?
22
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5answers
1k views

Can you overfit by training machine learning algorithms using CV/Bootstrap?

This question may well be too open ended to get a definitive answer, but hopefully not. Machine learning algorithms, such as SVM, GBM, Random Forest etc, generally have some free parameters that, ...
12
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4answers
8k views

How and why do normalization and feature scaling work?

I see that lots of machine learning algorithms work better with mean cancellation and covariance equalization. For example, Neural Networks tend to converge faster, and K-Means generally gives better ...
16
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2answers
2k views

Variance estimates in k-fold cross-validation

K-fold cross-validation can be used to estimate the generalization capability of a given classifier. Can I (or should I) also compute a pooled variance from all validation runs in order to obtain a ...
7
votes
2answers
3k views

How to statistically compare the performance of machine learning classifiers?

Based on estimated classification accuracy, I want to test whether one classifier is statistically better on a base set than another classifier . For each classifier, I select a training and testing ...
11
votes
3answers
1k views

What happens when you apply SVD to a collaborative filtering problem? What is the difference between the two?

In Collaborative filtering, we have values that are not filled in. Suppose a user did not watch a movie then we have to put an 'na' in there. If I am going to take an SVD of this matrix, then I have ...
1
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1answer
721 views

How does a uniform prior lead to the same estimates from maximum likelihood and mode of posterior?

I am studying different point estimate methods and read that when using MAP vs ML estimates, when we use a "uniform prior", the estimates are identical. Can somebody explain what a "uniform" prior is ...
12
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2answers
2k views

How to get started with neural networks

I'm completely new to neural networks but highly interested in understanding them. However it's not easy at all to get started. Could anyone recommend a good book or any other kind of resource? Is ...
31
votes
8answers
1k views

How can I help ensure testing data does not leak into training data?

Suppose we have someone building a predictive model, but that someone is not necessarily well-versed in proper statistical or machine learning principles. Maybe we are helping that person as they are ...
23
votes
3answers
15k views

Things to consider about masters programs in statistics

It is admission season for graduate schools. I (and many students like me) am now trying to decide which statistics program to pick. What are some things those of you who work with statistics ...
16
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2answers
2k views

Cross Validation (error generalization) after model selection

Note: Case is n>>p I am reading Elements of Statistical Learning and there are various mentions about the "right" way to do cross validation( e.g. page 60, page 245). Specifically, my question is how ...
26
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3answers
18k views

How to calculate precision and recall for multiclass classification using confusion matrix?

I wonder how to compute precision and recall using a confusion matrix for a multi-class classification problem. In specific, one observation can only be assigned with most probable class / label. I ...
12
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2answers
956 views

Supervised learning with “rare” events, when rarity is due to the large number of counter-factual events

Suppose you get to observe "matches" between buyers and sellers in a market. You also get to observe characteristics of both buyers and sellers which you would like to use to predict future matches ...
8
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1answer
927 views

Least angle regression keeps the correlations monotonically decreasing and tied?

I'm trying to solve a problem for least angle regression (LAR). This is a problem 3.23 on page 97 of Hastie et al., Elements of Statistical Learning, 2nd. ed. (5th printing). Consider a regression ...
2
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2answers
2k views

Estimate ARMA coefficients through ACF and PACF inspection

I know that this is probably a question that's been asked plenty of times, but i haven't seen an answer that's both accurate and simple. How do you estimate the appropriate forecast model for a time ...
11
votes
4answers
2k views

Hold-out Validation vs K-Fold Validation?

To me, it seems that Hold-out validation is useless. That is, splitting the original dataset into two-parts (training and testing) and using the testing score as a generalization measure, is somewhat ...
8
votes
1answer
4k views

Clustering: Should I use the Jensen-Shannon Divergence or its square?

I am clustering probability distributions using the Affinity Propagation algorithm, and I plan to use Jensen-Shannon Divergence as my distance metric. Is it correct to use JSD itself as the distance, ...
6
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2answers
349 views

Is KNN a discriminative learning algorithm?

It seems that KNN is a discriminative learning algorithm but I can't seem to find any online sources confirming this. Is KNN a discriminative learning algorithm?
6
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3answers
4k views

Comparing two classifier accuracy results for statistical significance with t-test

I want to compare the accuracy of two classifiers for statistical significance. Both classifiers are run on the same data set. This leads me to believe I should be using a one sample t-test from what ...
8
votes
3answers
6k views

Best way to perform multiclass SVM

I know that the SVM is a binary classifier. I would like to extend it to multi-class SVM. Which is the best, and maybe the easiest, way to perform it? code: in MATLAB ...
5
votes
1answer
319 views

What do “real values” refer to in supervised classification?

I'm using supervised classification algorithms from mlpy to classify things into two groups for a question-answering system. I don't really know how these algorithms work, but they seem to be doing ...
4
votes
3answers
509 views

Is a lower training accuracy possible in overfitting (one class SVM)

I am using the heart_scale data from LibSVM. The original data includes 13 features, but I only used 2 of them in order to plot the distributions in a figure. Instead of training the binary ...
3
votes
2answers
179 views

Detecting Bimodal Distribution

I have histograms of audio signals where they have bimodal "normal" distribution. What I want to do is to detect these subpopulations inorder to have a threshold, this is meant to divide the values ...
3
votes
2answers
644 views

Trouble applying hidden Markov models

Edit: I updated the question to hopefully make it more easy to understand. I think it was overly complex. I’m having a problem applying hidden Markov models to a game I’m building to learn about ...
1
vote
1answer
114 views

Weka - Result interpretation

I am running the classify in Weka for a certain dataset and I've noticed that if I'm trying to predict a nominal value the output specifically shows the correctly and incorrectly predicted values. ...
39
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1answer
6k views

ROC vs precision-and-recall curves

I understand the formal differences between them, what I want to know is when it is more relevant to use one vs. the other. Do they always provide complementary insight about the performance of a ...
16
votes
12answers
20k views

Best books for an introduction to statistical data analysis?

I bought this book: How to Measure Anything: Finding the Value of Intangibles in Business and Head First Data Analysis: A Learner's Guide to Big Numbers, Statistics, and Good Decisions What ...
32
votes
5answers
9k views

How to compute precision/recall for multiclass-multilabel classification?

I'm wondering how to calculate precision and recall measures for multiclass multilabel classification, i.e. classification where there are more than two labels, and where each instance can have ...
16
votes
6answers
2k views

Alternatives to classification trees, with better predictive (e.g: CV) performance?

I am looking for an alternative to Classification Trees which might yield better predictive power. The data I am dealing with has factors for both the explanatory and the explained variables. I ...
24
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6answers
2k views

Data mining: How should I go about finding the functional form?

I'm curious about repeatable procedures that can be used to discover the functional form of the function y = f(A, B, C) + error_term where my only input is a set of ...
20
votes
2answers
2k views

Sites for predictive modeling competitions

I participate in predictive modeling competitions on Kaggle, TunedIt, and CrowdAnalytix. I find that these sites are a good way to "work-out" for statistics/machine learning. Are there any other ...
20
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2answers
797 views

How to choose between learning algorithms

I need to implement a program that will classify records into 2 categories (true/false) based on some training data, and I was wondering at which algorithm/methodology I should be looking at. There ...
13
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6answers
4k views

What math subjects would you suggest to prepare for data mining and machine learning?

I'm trying to put together a self-directed math curriculum to prepare for learning data mining and machine learning. This is motivated by starting Andrew Ng's machine learning class on Coursera and ...