# 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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### How to compare the accuracy of two different models using statistical significance

I am working on time series prediction. I have two data sets $D1=\{x_1, x_2,....x_n\}$ and $D2=\{x_n+1, x_n+2, x_n+3,...., x_n+k\}$. I have three prediction models: $M1, M2, M3$. All of those model ...
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### How can I get feature importance for Gaussian Naive Bayes classifier?

I have a dataset consisting of 4 classes and around 200 features. I have implemented a Gaussian Naive Bayes classifier. I want now calculate the importance of each feature for each pair of classes ...
1answer
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### inferring most important features

Given a set of $n$ instances. For each instance I have a feature vector consisting of $m$ (numerical) features ($x_1$, $x_2$,...,$x_m$), n>>m. Moreover, for each instance I have a numerical score $y$ (...
3answers
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### Using ML to assist human labelling in dataset with highly unbalanced classes

Are there scientific issues with using ML to assist human annotation? I've got a 3 class unlabelled dataset where only 1 in 500 elements belong to the 2 classes of interest. The labels arn't ...
0answers
363 views

### Baseline for Precision-Related Metrics

When working with ROC-AUC as a metric for binary classification, one often considers a value of 0.5 as a baseline from a random classifier (i.e. a data-blind classifier that randomly classifies test ...
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582 views

### Machine learning with ordered labels

The usual method for adapting binary classifiers like various SVMs to multilabel data is one-vs-all, which assumes that labels are independent and in case of a prediction error we don't care what ...
1answer
842 views

### Computing a bootstrap confidence interval for the prediction error with the percentile and the BCa method

I have two related questions regarding the computation of a non-parametric bootstrap confidence interval for the prediction error. Setting: I have a sample S from a data population P and a learner L, ...
1answer
634 views

### Random Forest: Class specific feature importance

I'm using the bigrf R-package to analyse a dataset with ca. 50.000 observations x 120 variables, classified into two groups. After growing a forest of 1000 trees, ...
0answers
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### Convolutional neural network for multi-variate time series?

I want to use CNN architectures for classification of multivariate time-series, where we apply one label to each sequence. I searched the net for the available designs in the literature and i found ...
0answers
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### Why decision boundary differs between multinomial (softmax) and One-vs-Rest Logistic Regression for multiclass classification

Can someone please explain why the decision boundary differs between multinomial (softmax) and One-vs-Rest Logistic Regression for multiclass classification. Example shown below http://scikit-learn....
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### Effectiveness of Standardization and Normalization in Machine Learning

I am just studying the basics of machine learning and had a question about the standardisation and normalisation of the features and its effectiveness. I have read this CrossValidated question and ...
0answers
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### Adversarial examples - regularization method

In Intriguing properties of neural networks (https://arxiv.org/pdf/1312.6199.pdf) they show (4.3), that the existance of adversarial examples is closely connected to the upper Lipschitz constant, ...
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### How to include negative examples in multi-class classification?

I have a problem similar to this question: How do I use negative examples (in addition to positive ones) for training a multiclass softmax classifier (or a neural net with softmax output)? where I ...
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### classification: concatenating descriptors vs. using multiple classifiers

Consider a typical machine learning problem where you try to do object classification from a high-dimensional set of features. Suppose we know that the features are actually a collection of distinct "...
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### Google gender-pay gap vs

Background: I read this: google schools US government about gender pay gap. It derives from this google blog post by Eileen Naughton, VP of People Operations. She asserts that google is somehow "...
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### Unsupervised Anomaly Detection Threshold Selection

If we have a data set that contains only positive examples I am wondering how we can effectively choose a threshold for an anomaly detection technique. Are there anomaly detection techniques that can ...
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### Stop Word removal with RNNs

This might be a dumb question, but is it required or encouraged to remove stop words from documents when using Recurrent Neural Networks for Text Classification? To my understanding RNNs are able to "...