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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One-Class SVM - Decision function

The following is based on the paper: Schölkopf et.al - SVM for Novelty Detection First let us consider the (classical) Soft Margin SVM optimization problem: ${\displaystyle {\text{minimize }}{\frac {...
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Figuring out the margin for the soft margin SVM (exam question)

This is an exam question and I am not sure whether it is solveable with the given information. We were given a graphic that displayed binary labelled points $x^{(i)}\in \mathbb{R}^2$ with $y^{(i)} \...
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How to model probabilistic inputs with continuous output using regression

I have trained a multi-output classifier that takes an image as input and returns softmax logits as output. To be specific, the multi-output classifier takes an image and says the probability that ...
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Proof for asymptotic error in logistic regression

Ng, A.Y., and Jordan, M.I. (2001). On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes. Advances in Neural Information Processing Systems, 14, pp. 841-8, ...
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Overfitting in Random Forest Classifier?

I would like some help from you in a classification model that I am developing. In summary, the problem is: – Classification problem with binary outcome (0/1) – The classifier is a Random Forest ...
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How to compute gain statistic for the multinomial Naive Bayes classifier from Jurafsky and Martin (2018)

I'm trying to figure out how to compute the gain statistic G(w) following the fitting of the multinomial Naive Bayes model. This statistic is described on p17 of the new edition of Jurafsky and ...
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PCA, SMOTE and cross validation - how to combine them together?

I was reading a lot recently about PCA and cross validation and it seems that the majority call it malpractice to do PCA before cross validation. I would also like to perform SMOTE, but there is a ...
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Applying boosting to predictions from a Random Forest

I have a class of datasets for a binary classification problem where it is known that Random Forest performs poorly compared with GBM or FFNN, rarely adding anything to an ensemble. I've had an idea ...
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772 views

Correct cross-validation procedure for single model applied to panel data

Questions What is the correct CV procedure for panel data? I've been thinking of the problem as cross-validating a model fit to multiple time series data. Is the "population informed" CV procedure ...
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755 views

Measure agreement among experts in multi-label classification task

I was wondering whether there is a metric that can be used in order to compute the agreement, and therefore something like an upper bound for classifiers, among expert-labelled data. Assume there is ...
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How does one design a data set from polynomial target function such that logistic regression separates the data perfectly?

I want to design a target function for a classification task of the form: $$ f_{target}(x) = \mathbb{1}_{>0}[\sum^{D^*}_{i=0} w^*_i x^i] = \mathbb{1}_{>0}[ \langle w^*, \Phi(x)\rangle]$$ and ...
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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 ...
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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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Convert predicted probabilities after downsampling to actual probabilities in classification

If I use undersampling in case of an unbalanced binary target variable to train a model, the prediction method calculates probabilities under the assumption of a balanced data set. I discovered two ...
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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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multiclass classification having class imbalance with Gradient Boosting Classifier

I am using Abalon data for classification from UCI(https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data). I have scaled data and used TSNE for visualization. ...
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Choice of classification loss function with unequal payoffs

Suppose that I'm building a binary classifier parameterized by $\theta \in \mathbb{R}^k$ that maps some observed features $x_i \in \mathbb{R}^l$ to a decision of whether or not to play a game with an ...
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Stop Word removal with RNNs

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 "understand" words in a context and ...
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Can I use HMM to predict the spread of Ebola?

1) Can Hidden Markov Model be used across both a large number of categories (districts) and cases (weeks)? 2) Is HMM appropriate for trying to model such a problem? 3) Would I need to develop a ...
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Training instances importance in Random Forest?

Is it possible to determine the importance of the training examples in Random Forests, analogously to what's done with predictors? Basically the idea would be to find important samples in the data, ...
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How to retrain a production classifier that blocks its own positive examples?

I'm looking for help understanding how to re-train a fraud detection classifier that's been deployed to production (where it successfully blocked much, but not all fraud coming into the system). I ...
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How to tune the weak learner in boosted algorithms

It is commonly said that boosted algorithms (adaboost, gradient boosted trees) are composed of many "weak" learners. Let's stick to decision trees as the base learners. Some empirical studies ...
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Combining one class classifiers to do multi-class classification

I am working on a 3-class classification problem. The classifier I'm using is Bayesian Networks which provides me with a classification accuracy of around 60%. When I do a two-class classification, I ...
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Understanding calibrating probabilities using R

I am trying to understand R's calibration(package:caret) function. My main interest is binary classification. Calibration function is used for plotting true ...
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Multiclass vs. One-vs-All vs. One-vs-One classification

I am working on a classification problem with 7 classes. Is there any rationale to suspect that the best model might be found with a multiclass classifier, multiple one-vs-all classifiers, or even a ...
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A statistical test to measure the importance of features?

I'm currently trying to assess importance of the features for my classifier. The situation is the following: first I train my classifier with all of the features I have and tested on a test set . Then ...
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How to draw plot of the values of decision function of multi class svm versus another arbitrary values?

I am trying to draw a plot of the decision function ($f(x)=sign(wx+b)$ which can be obtain by fit$decision.values in R using the svm function of e1071 package) versus another arbitrary values. From ...
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False positives and False negatives of three or more classes

I have a $10\times 10$ confusion matrix $M$ generated after to execute an KNN classification process for digits recognition (0,1,2...9). As usual, each row of $M$ represent the "true/real" class of ...
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How to use information about likelihood of classes in a classifier?

General question: How can information about the likelihood of classes be used to improve a classifier? Suppose that the probability of each class is known quite precisely (from a very large sample), ...
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Student classification with Multinomial Logit

I’m analyzing student performance data. In my dataset each row corresponds to a student and each column contains several performance metrics (continuous) and the student type (categorical, 4 types). ...
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Who will follow who based on tags?

Suppose users in a system like a social network are described by a number of tags. The number of tags can be assumed to be less than 10. Example John: funny musician geek professor Peter: skinny ...
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Time series prediction where each datapoint has a sequence

I am Computer Science major, and new to stats, so please bear with me and point me to the right direction if what I'm asking is pretty obvious. I have a dataset, where each data point consists of <...
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Hierarchical classification

I'm currently working on the classification with massive amount of data. Similar to the kaggle one. Data input consist of features and multiple labels that can be hierarchically aligned. At first I ...
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Easy to follow tutorial on using Markov Random Fields for classifying pixels in gray-scale images

I am trying to learn how to use Markov Random Fields for classifying pixels in an image. Could someone please direct me to a simple tutorial demonstrating how this is done. The tutorial needs to ...
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Linear Discriminant Analysis: Using subject as classification

I have a problem where I need to identify from which subject a particular set of data points came. More specifically, my problem is that I need to demonstrate that my subjects (N=9) can be ...
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SVM classifier (with soft-margin) implementation in R, gamma value and quadprog

I'm trying to implement a Support Vector Machine classifier in R and I have to solve the optimization problem using the quadprog R package which solves problems of the form : $$min_b \frac{1}{2} b^...
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Determining conserved features using a Bayesian approach

I would like to perform some sort of binary classification, and my data set consists of 100 examples (for each class), which are vectors with 2500 elements. Ideally, I would like to determine which ...
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Classifiers with post-training constraints on the prediction space

I'm familiar with using tools like SVMs and decision trees for discrete classification problems. But one detail that I have not encountered in that domain is: what do you do if your classifier must ...
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Independent variable misclassification and statistical tests

Suppose we have our standard DGP, $y=\alpha+\beta x+\varepsilon,$ where $x$ is binary. Let's say the observed $x$ is actually measured with error, so that the explanatory variable is misclassified for ...
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Adjusting the classification threshold of Naive Bayes

I've been involved in a machine learning project recently and am now in the process of writing the project up for a paper submission. We used the naive bayes classifier on the project and developed a ...
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Category selection for text classification

It is said that to achieve a good result (many different metrics) for text classification, it is not always a business of selecting the algorithm/classifier. Sometimes, it is even more important to ...
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Using QDA for Non-Gaussian distributions

I am evaluating a Quadratic Discriminant Analysis (QDA) classifier on a high-dimensionality feature set. The features come from highly non-Gaussian distributions. However, when I transform the ...
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What methods can be used to transform data?

I am solving a binary classification problem with 4 predictor variables. The variables didn't seem to be linearly separable. I have used Neural Networks and Kernel SVM which work and give desired ...
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Test for Statistical Significance in the Accuracy of a Machine Learning System

I have what I imagine is an elementary question about evaluating statistical significance, but while I know a lot about probability I can't t-test my way out of a paper bag. From here I'm hoping to ...
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This decision is the best we can do if we have no prior information about the probabilities of the two classes?

I am currently studying the textbook Learning with kernels: support vector machines, regularization, optimization and beyond by Schölkopf and Smola. Chapter 1.2 A Simple Pattern Recognition Algorithm ...
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Is an off diagonal ROC curve not always better than random?

I'm trying to better understand the ROC when used for ML model classification and was looking at this explaining curve, explaining what is better and worse. However, I am thinking, contrary what is ...
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Why we use precision/recall in binary classification but sensitivity(=recall)/specificity in medicine?

Sensitivity=recall is used in both fields, but the second metric is different. Why? Both tasks (classification and medicine) look same - data has two classes and we do some predictions on it and want ...
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Graphical representation of Bayes decision boundary

Here is my problem statement: Let $X=(X_1,X_2)∈[0,1]×[0,1]$ and $Y∼Bernoulli(p=X_1⋅X_2)$. Plot the Bayes decision boundary ${(x1_,x_2):P(Y=1|X=(x_1,x_2))=0.5}$ and indicate the regions in $[0,1]×[0,1]$...
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Sample size vs F1-score, which is more important (small sample size)

I work in a filed were there are many publications based on classifiers trained on small samples sizes (but large amount of features). In most cases the sample size can only be increased by a few ...

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