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

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56
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5answers
11k views

Training with the full dataset after cross-validation?

Is it always a good idea to train with the full dataset after cross-validation? Put it another way, is it ok to train with all the samples in my dataset and not being able to check if this particular ...
221
votes
17answers
64k 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 ...
44
votes
14answers
7k 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 ...
123
votes
12answers
81k 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 ...
112
votes
9answers
93k 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 ...
43
votes
8answers
8k 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 ...
62
votes
7answers
15k views
+100

Obtaining knowledge from a random forest

Random forests are considered to be black boxes, but recently I was thinking what knowledge can be obtained from a random forest? The most obvious thing is the importance of the variables, in the ...
165
votes
2answers
27k views
+50

How to understand the drawbacks of K-means

K-means is a widely used method in cluster analysis. In my understanding, this method does NOT require ANY assumptions, i.e., give me a dataset and a pre-specified number of clusters, k, and I just ...
57
votes
2answers
18k 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 ...
30
votes
7answers
7k 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. ...
9
votes
1answer
2k 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 ...
62
votes
1answer
3k 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. ...
4
votes
1answer
5k views

How to interpret error measures in Weka output?

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. ...
80
votes
5answers
11k 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 ...
12
votes
2answers
3k 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 ...
17
votes
5answers
7k 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 ...
19
votes
1answer
4k views

Variance and bias in cross-validation: why does leave-one-out CV have higher variance?

How do different cross-validation methods compare in terms of model variance and bias? My question is partly motivated by this thread: Optimal number of folds in $K$-fold cross-validation. The ...
38
votes
3answers
11k 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 ...
24
votes
3answers
3k 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 ...
27
votes
5answers
2k 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, ...
8
votes
4answers
1k views

Are there any non-distance based clustering algorithms?

It seems that for K-means and other related algorithms, clustering is based off calculating distance between points. Is there one that works without it? Thanks!
19
votes
2answers
9k views

Linear kernel and non-linear kernel for support vector machine?

When using support vector machine, are there any guidelines on choosing linear kernel vs. nonlinear kernel, like RBF? I once heard that non-linear kernel tends not to perform well once the number of ...
14
votes
3answers
713 views

Can Random Forest be used for Feature Selection in Multiple Linear Regression?

Since RF can handle non-linearity but can't provide coefficients, would it be wise to use Random Forest to gather the most important Features and then plug those features into a Multiple Linear ...
10
votes
3answers
4k 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
votes
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 ...
3
votes
1answer
210 views

How to calculate decision boundary from support vectors?

I want to obtain decision boundary of SVM using OpenCV 2.4.11, but it seems that it's not returning it explicitly, but only support vectors. How we can calculate decision boundary from support ...
3
votes
1answer
1k 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 ...
116
votes
7answers
14k views

Why is Euclidean distance not a good metric in high dimensions?

I read that 'Euclidean distance is not a good distance in high dimensions'. I guess this statement has something to do with the curse of dimensionality, but what exactly? Besides, what is 'high ...
60
votes
11answers
33k 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 ...
17
votes
4answers
16k 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
votes
2answers
3k 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
votes
5answers
1k views

What exactly is a Bayesian model?

Can I call a model wherein Bayes' Theorem is used a "Bayesian model"? I am afraid such a definition might be too broad. So what exactly is a Bayesian model?
6
votes
2answers
8k 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
2answers
6k 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 ...
14
votes
3answers
2k 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 ...
3
votes
3answers
340 views

Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and how this makes linear separation possible

I am trying to understand the intuition behind kernel SVM's. Now, I understand how linear SVM's work, whereby a decision line is made which splits the data as best it can. I also understand the ...
68
votes
4answers
22k views

Bagging, boosting and stacking in machine learning

What's the similarities and differences beetween this 3 methods: bagging, boosting, stacking? Which is the best one? And why? Can you give me an example for each?
47
votes
4answers
19k 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?
31
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5answers
4k views

What makes the Gaussian kernel so magical for PCA, and also in general?

I was reading about kernel PCA (1, 2, 3) with Gaussian and polynomial kernels. How does the Gaussian kernel separate seemingly any sort of nonlinear data exceptionally well? Please give an intuitive ...
15
votes
3answers
6k views

Cross-validation or bootstrapping to evaluate classification performance?

What is the most appropriate sampling method to evaluate the performance of a classifier on a particular data set and compare it with other classifiers? Cross-validation seems to be standard practice, ...
19
votes
2answers
4k 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 ...
4
votes
2answers
3k views

Weighting more recent data in Random Forest model

I'm training a classification model with Random Forest to discriminate between 6 categories. My transactional data has approximately 60k+ observations and 35 variables. Here's an example of how it ...
7
votes
4answers
972 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?
1
vote
2answers
295 views

Do we need gradient descent to find the coefficients of a linear regression model

I was trying to learn machine learning using the coursera material Andrew Ng uses gradient descent algorithm to find the coefficients of the linear regression model that will minimize the error ...
3
votes
2answers
494 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 ...
1
vote
2answers
380 views

How to bootstrap panel data?

I'm fitting some machine learning algorithms (e.g. SVM) on my panel data. It's taking too long for my entire dataset, so I'm considering generating smaller samples from bootstrapping then fit the SVM ...
16
votes
2answers
3k 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 ...
46
votes
3answers
36k 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 ...
37
votes
8answers
2k 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 ...
17
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6answers
12k 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 ...