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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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 ...
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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, ...
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Logistic regression for classification: are there any analytical solutions for the out-of-sample accuracy?

I run a binary logistic regression, with a binary dependent variable and a continuous independent one. Now I want to evaluate the out-of-sample performance of the classification algorithm so obtained. ...
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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, ...
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Precision and recall of a random classifier

My understanding of precision and recall tells me that there is a tradeoff between these two measures: you can improve one at the cost of the other. However, when I think of a random classifier (on a ...
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Is there a ML or DL tool that can learn to detect periodically occurring patterns in a one dimensional time series?

I am trying to create a tool that labels refrigerator temperature readings. A reading is taken every 5 minutes, and its label identifies whether of not it was taken while the refrigerator was ...
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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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When is there a free lunch?

The no free lunch theorem (NFL) states that Theorem (Wolpert and Macready 1997) Let $A$ be any learning algorithm for the task of binary classification with respect to the $0−1$ loss over a ...
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When is oversampling poor practice?

For my particular domain and problem, I have data on the entire population. However, my "event" only occurs in 0.5% of the cases. I want my model to be able to pick up on significant characteristics ...
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when can I substitute an inverse with a pseudo-inverse in an estimator

Short Version: can I substitute the Moore-Penrose generalized inverse of a matrix (R function ginv()) for a matrix inverse (R function ...
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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 ...
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Features for binary time-series event prediction

This question is somewhat inspired by the answer to Features for time series classification. The difference to that question is that I have a dataset with multi-dimensional time-series where I have ...
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How to combine noisy and noise-free datasets to train a model

Overview Suppose I have two datasets, both of which consist of rows of features and their matching labels. One of these datasets is noise-free and its labels correspond to the ground truth, but the ...
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Improve precision/recall for class imbalance?

Trying to get better precision/recall for both classes ... any tips? I have heterogeneous features [a few num vars, a few cat vars, and 2 text vars] Target is a binary classification w/ class ...
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How to predict routes using clustering data

I've been working on a ship route prediction algorithm such that given the past and current trajectory of a ship I am able to estimate the future one. The trajectories are represented as a sequence of ...
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Multi-label classification: Predict product category

I want to predict to which product category a product belongs. A total of 400k products need to be translated from the old (less refined) to the new product category tree. (E.g. alarm clock used to ...
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True positive, false negative, true negative, false positive definitions for multiclass-multilabel classification?

I'm trying to apply some evaluation metrics to several clustering methods. I thought that I knew them basing on the multiclass confusion matrix, considering the rows as the actual classes and the ...
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Micro vs weighted F1 score

In a multi-label or multi-class classification setting, when choosing between a micro or a weighted F1 score, what shall I take into account? The main upside of choosing macro is that one gets a ...
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why use diagonal $\Sigma$ when working with Bayes decision theory?

My prof. said in the class that for Bayes decision rule, the likelihood is Gaussian and in practice, we will almost always work with a diagonal $\Sigma$. Why is that? I know that a diagonal $\Sigma$ ...
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Logistic regression and maximum entropy

I have read (e.g. here) that a (multinomial) logistic regressor corresponds to a maximum entropy classifier. My question is, how does one end up with the formula for logistic regression starting with ...
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Combining posterior probabilities from multiple classifiers

I am new to machine learning and can't get my head around this problem. I have two patient datasets, the first ($D_1$) contains $Y,Z,X$ that convey blood-sample information and the second ($D_2$) ...
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k-fold cross validation vs k times hold-out validation

I am facing the evaluation of a genetic programming algorithm. I am using the Proben1 cancer1 dataset to evaluate the models created by this algorithm. This dataset contains 699 samples, which is ...
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Reasons for transforming multiple class classification problem into a set of binary sub-problems?

Does anyone know of a good reference that list the reasons for transforming multiple class classification problem into a set of binary sub-problems? In response to comment: One reason to transform a ...
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Computation of log-likelihood in semi-supervised naive bayes

I have the following 2 questions about log-likelihood computation in semi-supervised Naive Bayes. I have read on several documents online that, in every EM iteration of the semi-supervised Naive ...
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Classification of multiple time series and case level attributes

I'm pretty new to machine learning so wondering whether someone can help check my thinking or point me in the right direction! I need to create a classifier which can predict an outcome for a person ...
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Maximum entropy classifier and sentiment analysis

I am doing a project work in sentiment analysis (on Twitter data) using machine learning approach. In order to find the 'best' way to this I have experimented with naive Bayesian and maximum entropy ...
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How to subset alternatives in nested multinomial logistic regression?

I am trying to predict whether or not captains in a particular groundfish fishery choose to fish on any given day and what variables may influence that decision. Originally I had planned on using ...
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6 votes
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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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609 views

"Hierarchical" Random forests?

Background I am using Random Forest to classify ~900 objects based on a large number (> 80) predictors. I split these 70:30 for training and testing. The overall model does fairly well, giving an ...
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6 votes
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Minimum training sample size required for a classifier

What is the best method to determine the minimum number of training samples required for a classifier? I am only comparing one classifier (four class problem), discriminant function analysis (DFA) ...
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692 views

What is the difference of "normal" F1 and macro average F1 score with binary classification

Please note that I always talk about binary classification here. I do not speak about multi class classification. In case of unbalanced binary datasets it is a good practice to use F1 score. While ...
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Is stratified sampling and oversampling contradictory in imbalanced datasets?

I am confused with the concepts of stratified sampling and oversampling for imbalanced datasets. From what I read in this question here: why-use-stratified-cross-validation-why-does-this-not-damage-...
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Confusion matrix for multilabel classification

I know that a similar subject was treated here, but my question is a little bit different. I have a result of multilabel classification, like this (2 observations, 3 labels in the example, in ...
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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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1 answer
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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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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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How do I make use of "soft" labels in binary classification?

Let's say we have a binary classification task, but our dataset contains more fine grained values of how much an examples belongs to the class or not. So the labels are real numbers in $\left[0,1\...
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Probability distribution over classes as labels in classification task

Classical classification problem has next formulation. Given a set of $n$ attributes, a set of $k$ classes and a set of labelled training instances: $(i_i, l_j),...,(i_j, l_j)$, where $ i = (v_1, v_2,...
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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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logistic regression prediction: changing interpretation with changing prior

The data include 3 equally sized subsets A, B and C, belonging to two classes: A belongs to class 1. B and C belong to class 2. The prior probabilities of an observation coming from class 1 ...
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Estimating parameters using Kullback-Leibler or Kolmogorov-Smirnoff via Nelder-Mead

I want to find the parameters of a model which specifies a set of classification probabilities, for say M classes. (I'll use the parameters in another model later.) Given a set of parameters $\theta$,...
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Transferring to new domain

Suppose I have a set of characters with both natural scene and synthetic images, and another set with only synthetic images. I'd like to make a classifier which is trained on only on this data and ...
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1 answer
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Regarding the size of training data for building classifier

When we build a classifier, like SVM or Naive Bayesian, are there any generic rules or theoretical derivations on the size of training data set? For example, to train a SVM-based classifier, what ...
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Number of states and symbols in multi class Hidden Markov Model classifier

I'm designing a multi class classifier (for 4 classes) using Discrete HMMs with States N and Symbols M for each of the HMM. However, I found that recognition performance(i.e highest log likelihood) ...
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Asynchronous data stream matching

Suppose you have a classifier $C^n$ which continuously outputs a stream of classification labels $K^n_i$ and corresponding timestamps $T^n_i$. Also, we know the prior probability $P(K^n) \forall n$. ...
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Unlearning Neural Network? Prevent learning from a specific feature

Is it possible to train a NN to avoid the features that a different neural network finds? For example, let's train a simple 1 layer CNN with 1x1 kernels on a supervised binary classification problem. ...
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4 votes
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Predict churn in a range of time after observation window is finished

I'm building a churn model. Each user's historic data (observation window) is a constant period, but each observation window contains different dates. For example the next figure: Let's say, that the ...
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4 votes
1 answer
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What Cross Validation results actually tell about Bias and Variance?

I am trying to get a deeper understanding of the common ML pipelines and I have some doubts regarding Cross Validation, why do we really use it and what does it really tell us about Bias and Variance. ...
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Derivation of k nearest neighbor classification rule

One way to derive the k-NN decision rule based on the k-NN density estimation goes as follows: given $k$ the number of neighbors, $k_i$ the number of neighbors of class $i$ in the bucket, $N$ the ...
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