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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How does a Relevance Vector Machine (RVM) work?

Relevance Vector Machines (RVMs) are really interesting models when contrasted with the highly geometrical (and popular) SVMs. In the light of a question like How does a Support Vector Machine (SVM) ...
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Random forest is overfitting?

I'm experimenting with random forests with scikit-learn and I'm getting great results of my training set, but relatively poor results on my test set... Here is the problem (inspired from poker) which ...
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How LDA, a classification technique, also serves as dimensionality reduction technique like PCA

In this article , the author links linear discriminant analysis (LDA) to principal component analysis (PCA). With my limited knowledge, I am not able to follow how LDA can be somewhat similar to PCA. ...
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Semi-supervised learning, active learning and deep learning for classification

Final edit with all resources updated: For a project, I am applying machine learning algorithms for classification. Challenge: Quite limited labeled data and much more unlabeled data. Goals: Apply ...
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5answers
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Large scale text classification

I am looking to do classification on my text data. I have 300 classes, 200 training documents per class (so ...
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Social network datasets

I am looking for social network datasets (twitter, friendfeed, facebook, lastfm, etc.) for classification tasks, preferably in arff format. My searches via UCI and Google weren't successful so far......
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k-fold Cross validation of ensemble learning

I am confused about how to partition the data for k-fold cross validation of ensemble learning. Assuming I have an ensemble learning framework for classification. My first layer contains the ...
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Biased Data in Machine Learning

I am working on a Machine Learning project with data that is already (heavily) biased by data selection. Let's assume you have a set of hard coded rules. How do you build a machine learning model to ...
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1answer
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Opinions about Oversampling in general, and the SMOTE algorithm in particular [closed]

What is your opinion about oversampling in classification in general, and the SMOTE algorithm in particular? Why would we not just apply a cost/penalty to adjust for imbalance in class data and any ...
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Benefits of stratified vs random sampling for generating training data in classification

I would like to know if there are any/some advantages of using stratified sampling instead of random sampling, when splitting the original dataset into training and testing set for classification. ...
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3answers
687 views

Is building a multiclass classifier better than several binary ones?

I need to classify URLs into categories. Say I have 15 categories that I'm planning to zero down every URL to. Is a 15-way classifier better? Where I have 15 labels and generate features for each ...
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When does Naive Bayes perform better than SVM?

In a small text classification problem I was looking at, Naive Bayes has been exhibiting a performance similar to or greater than an SVM and I was very confused. I was wondering what factors decide ...
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What is the difference between SVM and LDA?

What is the difference between Support Vector Machines and Linear Discriminant Analysis?
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When should I not use an ensemble classifier?

In general, in a classification problem where the goal is to accurately predict out-of-sample class membership, when should I not to use an ensemble classifier? This question is closely related to ...
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Why does ridge regression classifier work quite well for text classification?

During an experiment for text classification, I found ridge classifier generating results that constantly top the tests among those classifiers that are more commonly mentioned and applied for text ...
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Testing Classification on Oversampled Imbalance Data

I am working on severely imbalanced data. In literature, several methods are used to re-balance the data using re-sampling (over- or under-sampling). Two good approaches are: SMOTE: Synthetic ...
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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 ...
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Quiz: Tell the classifier by its decision boundary

Given are the 6 decision boundaries below. Decision boundaries is violett lines. Dots and crosses are two different data sets. We have to decide which one is a: Linear SVM Kernelized SVM (Polynomial ...
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I want to build a crime index and political instability index based in news stories

I have this side project where I crawl the local news websites in my country and want to build a crime index and political instability index. I have already covered the information retrieval part of ...
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Collinear variables in Multiclass LDA training

I'm training a Multi-class LDA classifier with 8 classes of data. While performing training, I get a warning of: "Variables are collinear" I'm getting a training accuracy of over 90%. I'm using ...
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What is null model in regression and how does it related to null hypothesis?

What is null model in regression and whats the relationship between null model and null hypothesis? For my understanding, does it mean Using "average of the response variable" to predict continuous ...
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4answers
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Low classification accuracy, what to do next?

So, I'm a newbie in ML field and I try to do some classification. My goal is to predict the outcome of a sport event. I've gathered some historical data and now try to train a classifier. I got around ...
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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?
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Grid search on k-fold cross validation

I've a dataset of 120 samples in a 10-fold cross validation setting. Currently, I pick the training data of the first holdout and do a 5-fold cross-validation on it to pick the values of gamma and C ...
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1answer
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What does it mean that AUC is a semi-proper scoring rule?

A proper scoring rule is a rule that is maximized by a 'true' model and it doesn't allow 'hedging' or gaming the system (deliberately reporting different results as is the true belief of the model to ...
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State of the art in general learning from data in '69

I'm trying to understand the context of the famous Minsky and Papert book "Perceptrons" from 1969, so critical to neural networks. As far as I know, there were no other generic supervised learning ...
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training approaches for highly-imbalanced data set

I have a highly-imbalanced test data set. The positive set consists of 100 cases while the negative set consists of 1500 cases. On the training side, I have a larger candidate pool: the positive ...
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1answer
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Training a basic Markov Random Field for classifying pixels in an image

I am attempting to learn how to use Markov Random Fields to segment regions in an image. I do not understand some of the parameters in the MRF or why the expectation maximisation I perform fails to ...
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What is the best out-of-the-box 2-class classifier for your application? [closed]

Rules: one classifier per answer vote up if you agree downvote/remove duplicates. put your application in the comment
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For linear classifiers, do larger coefficients imply more important features?

I'm a software engineer working on machine learning. From my understanding, linear regression (such as OLS) and linear classification (such as logistic regression and SVM) make a prediction based on ...
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What impact does increasing the training data have on the overall system accuracy?

Can someone summarize for me with possible examples, at what situations increasing the training data improves the overall system? When do we detect that adding more training data could possibly over-...
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What is a good resource that includes a comparison of the pros and cons of different classifiers?

What is the best out-of-the-box 2-class classifier? Yes, I guess that's the million dollar question, and yes, I'm aware of the no free lunch theorem, and I've also read the previous questions: What ...
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Increasing number of features results in accuracy drop but prec/recall increase

I am new to Machine Learning. At the moment I am using a Naive Bayes (NB) classifier to classify small texts in 3 classes as positive, negative or neutral, using NLTK and python. After conducting ...
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3answers
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Suggestions for cost-sensitive learning in a highly imbalanced setting

I have a dataset with a few million rows and ~100 columns. I would like to detect about 1% of the examples in the dataset, which belong to a common class. I have a minimum precision constraint, but ...
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Classification with tall fat data

I need to train a linear classifier on my laptop with hundreds of thousands of data points and about ten thousand features. What are my options? What is the state of the art for this type of problem? ...
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Statistical similarity of time series

Supposing one has a time series from which one can take various measurements such as period, maximum, minimum, average etc. and then use these to create a model sine wave with the same attributes, are ...
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Combining classifiers by flipping a coin

I am studying a machine learning course and the lecture slides contain information what I find contradicting with the recommended book. The problem is the following: there are three classifiers: ...
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What is “baseline” in precision recall curve

I'm trying to understand precision recall curve, I understand what precision and recall are but the thing I don't understand is the "baseline" value. I was reading this link https://classeval....
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What measure of training error to report for Random Forests?

I'm currently fitting random forests for a classification problem using the randomForest package in R, and am unsure about how to report training error for these ...
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Mean(scores) vs Score(concatenation) in cross validation

TLDR: My dataset is pretty small (120) samples. While doing 10-fold cross validation, should I: Collect the outputs from each test fold, concatenate them into a vector, and then compute the error on ...
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In statistical learning theory, isn't there a problem of overfitting on a test set?

Let's consider the problem about classifying the MNIST dataset. According to Yann LeCun's MNIST Webpage, 'Ciresan et al.' got 0.23% error rate on MNIST test set using Convolutional Neural Network. ...
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Neural networks vs everything else

I haven't found a satisfactory answer to this from google. Of course if the data I have is of the order of millions then deep learning is the way. And I have read that when I do not have big data ...
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1answer
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Depth of a decision tree

Since the decision tree algorithm split on an attribute at every step, the maximum depth of a decision tree is equal to the number of attributes of the data. Is this correct?
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Classification with Gradient Boosting : How to keep the prediction in [0,1]

The question I am struggling to understand how the prediction is kept within the $[0,1]$ interval when doing binary classification with Gradient Boosting. Assume we are working on a binary ...
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Can CART models be made robust?

A colleague in my office said to me today "Tree models aren't good because they get caught by extreme observations". A search here resulted in this thread that basically supports the claim. Which ...
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Intuition for Support Vector Machines and the hyperplane

In my project I want to create a logistic regression model for predicting binary classification (1 or 0). I have 15 variables, 2 of which are categorical, while the rest are a mixture of continuous ...
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3answers
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How to do data augmentation and train-validate split?

I am doing image classification using machine learning. Suppose I have some training data (images) and will split the data into training and validation sets. And I also want to augment the data (...
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3answers
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Choice of neural net hidden activation function

I have read elsewhere that one's choice of hidden layer activation function in a NN should be based on one's need, i.e. if you need values in the range -1 to 1 use tanh and use sigmoid for the range 0 ...
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3answers
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How to choose an error metric when evaluating a classifier?

I've seen different error metrics used in the Kaggle competitions: RMS, mean-square, AUC, amongst others. What's the general rule of thumb on choosing an error metric, i.e. how do you know which error ...
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Which statistical classification algorithm can predict true/false for a sequence of inputs?

Given a sequence of inputs, I need to determine whether this sequence has a certain desired property. The property can only be true or false, that is, there are only two possible classes that a ...