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# Questions tagged [classification]

Statistical classification is the problem of identifying the sub-population to which new observations belong, where the identity of the sub-population is unknown, on the basis of a training set of data containing observations whose sub-population is known. Therefore these classifications will show a variable behavior which can be studied by statistics.

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341k views

### What does AUC stand for and what is it?

Searched high and low and have not been able to find out what AUC, as in related to prediction, stands for or means.
90k 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 ...
115k views

### Cohen's kappa in plain English

I am reading a data mining book and it mentioned the Kappa statistic as a means for evaluating the prediction performance of classifiers. However, I just can't understand this. I also checked ...
56k 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 ...
36k 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. ...
131k views

### How do you 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. Specifically, an observation can only be assigned to its most probable class / label. I ...
113k views

### How to calculate Area Under the Curve (AUC), or the c-statistic, by hand

I am interested in calculating area under the curve (AUC), or the c-statistic, by hand for a binary logistic regression model. For example, in the validation dataset, I have the true value for the ...
155k views

### How to produce a pretty plot of the results of k-means cluster analysis?

I'm using R to do K-means clustering. I'm using 14 variables to run K-means What is a pretty way to plot the results of K-means? Are there any existing implementations? Does having 14 variables ...
29k 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 ...
7k 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. ...
18k 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 ...
57k views

### Best way to present a random forest in a publication?

I am using the random forest algorithm as a robust classifier of two groups in a microarray study with 1000s of features. What is the best way to present the random forest so that there is enough ...
38k views

### Why are neural networks becoming deeper, but not wider?

In recent years, convolutional neural networks (or perhaps deep neural networks in general) have become deeper and deeper, with state-of-the-art networks going from 7 layers (AlexNet) to 1000 layers (...
83k 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 ...
87k views

### How to plot ROC curves in multiclass classification?

In other words, instead of having a two class problem I am dealing with 4 classes and still would like to assess performance using AUC.
82k views

### Softmax vs Sigmoid function in Logistic classifier?

What decides the choice of function ( Softmax vs Sigmoid ) in a Logistic classifier ? Suppose there are 4 output classes . Each of the above function gives the probabilities of each class being the ...
11k 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 ...
17k views

### Alternatives to logistic regression in R

I would like as many algorithms that perform the same task as logistic regression. That is algorithms/models that can give a prediction to a binary response (Y) with some explanatory variable (X). ...
24k 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 ...
41k views

### Why not approach classification through regression?

Some material I've seen on machine learning said that it's a bad idea to approach a classification problem through regression. But I think it's always possible to do a continuous regression to fit the ...
36k views

### What is the difference between Multiclass and Multilabel Problem

What is the difference between a multiclass problem and a multilabel problem?
10k 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! ...
29k 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 ...
49k views

### Why is logistic regression a linear classifier?

Since we are using the logistic function to transform a linear combination of the input into a non-linear output, how can logistic regression be considered a linear classifier? Linear regression is ...
62k 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 ...
30k views

### Features for time series classification

I consider the problem of (multiclass) classification based on time series of variable length $T$, that is, to find a function f(X_T) = y \in [1..K]\\ \text{for } X_T = (x_1, \dots, x_T)\\ \text{...
12k views

### Random forest assumptions

I am kind of new to random forest so I am still struggling with some basic concepts. In linear regression, we assume independent observations, constant variance… What are the basic assumptions/...
35k views

### Training a decision tree against unbalanced data

I'm new to data mining and I'm trying to train a decision tree against a data set which is highly unbalanced. However, I'm having problems with poor predictive accuracy. The data consists of students ...
25k views

### Why downsample?

Suppose I want to learn a classifier that predicts if an email is spam. And suppose only 1% of emails are spam. The easiest thing to do would be to learn the trivial classifier that says none of the ...
99k views

### How to interpret F-measure values?

I would like to know how to interpret a difference of f-measure values. I know that f-measure is a balanced mean between precision and recall, but I am asking about the practical meaning of a ...
33k views

### Why do naive Bayesian classifiers perform so well?

Naive Bayes classifiers are a popular choice for classification problems. There are many reasons for this, including: "Zeitgeist" - widespread awareness after the success of spam filters about ten ...
49k views

### ImageNet: what is top-1 and top-5 error rate?

In ImageNet classification papers top-1 and top-5 error rates are important units for measuring the success of some solutions, but what are those error rates? In ImageNet Classification with Deep ...
44k views

### SVM, Overfitting, curse of dimensionality

My dataset is small (120 samples), however the number of features are large varies from (1000-200,000). Although I'm doing feature selection to pick a subset of features, it might still overfit. My ...
23k views

### Apply word embeddings to entire document, to get a feature vector

How do I use a word embedding to map a document to a feature vector, suitable for use with supervised learning? A word embedding maps each word $w$ to a vector $v \in \mathbb{R}^d$, where $d$ is some ...
63k views

### Improve classification with many categorical variables

I'm working on a dataset with 200,000+ samples and approximately 50 features per sample: 10 continuous variables and the other ~40 are categorical variables (countries, languages, scientific fields ...
34k views

### Logistic regression vs. LDA as two-class classifiers

I am trying to wrap my head around the statistical difference between Linear discriminant analysis and Logistic regression. Is my understanding right that, for a two class classification problem, LDA ...
73k views

### How to interpret OOB and confusion matrix for random forest?

I got a an R script from someone to run a random forest model. I modified and run it with some employee data. We are trying to predict voluntary separations. Here is some additional info: this is a ...
19k 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 ...
15k views

### Free data set for very high dimensional classification [closed]

What are the freely available data set for classification with more than 1000 features (or sample points if it contains curves)? There is already a community wiki about free data sets: Locating ...
69k views

### How to interpret Mean Decrease in Accuracy and Mean Decrease GINI in Random Forest models

I'm having some difficulty understanding how to interpret variable importance output from the Random Forest package. Mean decrease in accuracy is usually described as "the decrease in model accuracy ...
10k views

### Why is t-SNE not used as a dimensionality reduction technique for clustering or classification?

In a recent assignment, we were told to use PCA on the MNIST digits to reduce the dimensions from 64 (8 x 8 images) to 2. We then had to cluster the digits using a Gaussian Mixture Model. PCA using ...
29k 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!
35k views

### What is meant by 'weak learner'?

Can anyone tell me what is meant by the phrase 'weak learner'? Is it supposed to be a weak hypothesis? I am confused about the relationship between a weak learner and a weak classifier. Are both the ...
2k views

### Statistical classification of text

I'm a programmer without statistical background, and I'm currently looking at different classification methods for a large number of different documents that I want to classify into pre-defined ...
42k 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 ...
37k views

### Which search range for determining SVM optimal C and gamma parameters?

I am using SVM for classification and I am trying to determine the optimal parameters for linear and RBF kernels. For the linear kernel I use cross-validated parameter selection to determine C and for ...
23k views

### How is Naive Bayes a Linear Classifier?

I've seen the other thread here but I don't think the answer satisfied the actual question. What I have continually read is that Naive Bayes is a linear classifier (ex: here) (such that it draws a ...
5k views

### Do we have to tune the number of trees in a random forest?

Software implementations of random forest classifiers have a number of parameters to allow users to fine-tune the algorithm's behavior, including the number of trees in the forest. Is this a parameter ...
Given a dataset with instances $x_i$ together with $N$ classes where every instance $x_i$ belongs exactly to one class $y_i$ a multiclass classifier After the training and testing I basically have a ...