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Questions tagged [machine-learning]

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

83
votes
7answers
20k views

Why is accuracy not the best measure for assessing classification models?

This is a general question that was asked indirectly multiple times in here, but it lacks a single authoritative answer. It would be great to have a detailed answer to this for the reference. ...
41
votes
5answers
6k views

When is unbalanced data really a problem in Machine Learning?

We already had multiple questions about unbalanced data when using logistic regression, SVM, decision trees, bagging and a number of other similar questions, what makes it a very popular topic! ...
192
votes
3answers
17k views

How to know that your machine learning problem is hopeless?

Imagine a standard machine-learning scenario: You are confronted with a large multivariate dataset and you have a pretty blurry understanding of it. What you need to do is to make predictions ...
63
votes
2answers
13k views

Why isn't Logistic Regression called Logistic Classification?

Since Logistic Regression is a statistical classification model dealing with categorical dependent variables, why isn't it called Logistic Classification? Shouldn't the "Regression" name be reserved ...
397
votes
11answers
437k 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 ...
341
votes
5answers
100k views

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 ...
122
votes
5answers
36k 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 ...
47
votes
3answers
16k views

Can a 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 ...
30
votes
3answers
17k views

Classification probability threshold

I have a question regarding classification in general. Let f be a classifier, which outputs a set of probabilities given some data D. Normally, one would say: well, if P(c|D) > 0.5, we will assign a ...
407
votes
21answers
141k 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 ...
68
votes
7answers
27k views

Bias and variance in leave-one-out vs K-fold cross validation

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: is leave-...
131
votes
4answers
77k 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 ...
54
votes
17answers
10k 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 ...
48
votes
4answers
21k views

Class imbalance in Supervised Machine Learning

This is a question in general, not specific to any method or data set. How do we deal with a class imbalance problem in Supervised Machine learning where the number of 0 is around 90% and number of 1 ...
221
votes
7answers
61k 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 ...
30
votes
3answers
14k 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 ...
121
votes
9answers
46k views

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 ...
96
votes
5answers
48k views

How does a Support Vector Machine (SVM) work?

How does a Support Vector Machine (SVM) work, and what differentiates it from other linear classifiers, such as the Linear Perceptron, Linear Discriminant Analysis, or Logistic Regression? * (* I'm ...
48
votes
10answers
57k views

Hold-out validation vs. cross-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 ...
72
votes
8answers
26k 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 ...
32
votes
1answer
42k views

How to interpret error measures?

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. ...
12
votes
1answer
6k views

How to know if a learning curve from SVM model suffers from bias or variance?

I created this learning curve and I want to know if my SVM model suffers from bias or variance? How can I conclude that from this graph?
84
votes
6answers
29k views

Is it possible to train a neural network without backpropagation?

Many neural network books and tutorials spend a lot of time on the backpropagation algorithm, which is essentially a tool to compute the gradient. Let's assume we are building a model with ~10K ...
25
votes
2answers
6k 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. In this lecture, Andrew Ng uses gradient descent algorithm to find the coefficients of the linear regression model that will ...
25
votes
3answers
22k views

Why is AUC higher for a classifier that is less accurate than for one that is more accurate?

I have two classifiers A: naive Bayesian network B: tree (singly-connected) Bayesian network In terms of accuracy and other measures, A performs comparatively worse than B. However, when I use the R ...
17
votes
2answers
6k 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 ...
107
votes
5answers
17k 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 ...
73
votes
1answer
6k 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. ...
48
votes
6answers
17k views

Binary classification with strongly unbalanced classes

I have a data set in the form of (features, binary output 0 or 1), but 1 happens pretty rarely, so just by always predicting 0, I get accuracy between 70% and 90% (depending on the particular data I ...
159
votes
9answers
31k views

Why the sudden fascination with tensors?

I've noticed lately that a lot of people are developing tensor equivalents of many methods (tensor factorization, tensor kernels, tensors for topic modeling, etc) I'm wondering, why is the world ...
87
votes
4answers
61k 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?
55
votes
8answers
9k 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 ...
39
votes
2answers
56k 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 ...
13
votes
4answers
6k 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?
11
votes
1answer
5k views

Dropping one of the columns when using one-hot encoding

My understanding is that in machine learning it can be a problem if your dataset has highly correlated features, as they effectively encode the same information. Recently someone pointed out that ...
117
votes
5answers
161k views

What is the influence of C in SVMs with linear kernel?

I am currently using an SVM with a linear kernel to classify my data. There is no error on the training set. I tried several values for the parameter $C$ ($10^{-5}, \dots, 10^2$). This did not ...
108
votes
8answers
39k views

Why is Newton's method not widely used in machine learning?

This is something that has been bugging me for a while, and I couldn't find any satisfactory answers online, so here goes: After reviewing a set of lectures on convex optimization, Newton's method ...
48
votes
8answers
19k views

Book for reading before Elements of Statistical Learning?

Based on this post, I want to digest Elements of Statistical Learning. Fortunately it is available for free and I started reading it. I don't have enough knowledge to understand it. Can you recommend ...
61
votes
2answers
16k views

Why is ridge regression called “ridge”, why is it needed, and what happens when $\lambda$ goes to infinity?

Ridge regression coefficient estimate $\hat{\beta}^R$ are the values that minimize the $$ \text{RSS} + \lambda \sum_{j=1}^p\beta_j^2. $$ My questions are: If $\lambda = 0$, then we see that the ...
21
votes
3answers
15k 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, ...
36
votes
3answers
2k views

Variance of $K$-fold cross-validation estimates as $f(K)$: what is the role of “stability”?

TL,DR: It appears that, contrary to oft-repeated advice, leave-one-out cross validation (LOO-CV) -- that is, $K$-fold CV with $K$ (the number of folds) equal to $N$ (the number of training ...
28
votes
2answers
37k views

Relative importance of a set of predictors in a random forests classification in R

I'd like to determine the relative importance of sets of variables toward a randomForest classification model in R. The ...
24
votes
3answers
10k views

What are the impacts of choosing different loss functions in classification to approximate 0-1 loss

We know that some objective functions are easier to optimize and some are hard. And there are many loss functions that we want to use but hard to use, for example 0-1 loss. So we find some proxy loss ...
23
votes
1answer
4k views

Is there any algorithm combining classification and regression?

I'm wondering if there's any algorithm could do classification and regression at the same time. For example, I'd like to let the algorithm learn a classifier, and at the same time within each label, ...
32
votes
5answers
11k 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?
12
votes
3answers
13k views

What algorithms need feature scaling, beside from SVM?

I am working with many algorithms: RandomForest, DecisionTrees, NaiveBayes, SVM (kernel=linear and rbf), KNN, LDA and XGBoost. All of them were pretty fast except for SVM. That is when I got to know ...
11
votes
3answers
589 views

Statistical significance (p-value) for comparing two classifiers with respect to (mean) ROC AUC, sensitivity and specificity

I have a test set of 100 cases and two classifiers. I generated predictions and computed ROC AUC, sensitivity and specificity for both classifiers. Question 1: How can I compute p-value to check if ...
15
votes
1answer
2k views

What problem does oversampling, undersampling, and SMOTE solve?

In a recent, well recieved, question, Tim asks when is unbalanced data really a problem in Machine Learning? The premise of the question is that there is a lot of machine learning literature ...
6
votes
2answers
2k views

What is the justification for unsupervised discretization of continuous variables?

A number of sources suggest that there are many negative consequences of the discretization (categorization) of continuous variables prior to statistical analysis (sample of references [1]-[4] below). ...
7
votes
2answers
6k 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 ...