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 to use reservoir states for readout and training?

I’m trying to make a Liquid State Machine, I have a spiking neural network as the liquid, and a feedforward neural network that should learn to map the reservoir’s states to the output. I’ve read ...
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Role of coefficients in model selection for logistic regression

I have a model that I am using to predict mortality and it gives me an AUC of 0.799. The R code that I am using would look something like this: ...
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Using AUC to compare logistic lasso and elastic net

I've seen this question answered here but I do not understand the answer. Harrell recommends using deviance based measures. David Hand (referenced in the thread) says that that the AUC is ...
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388 views

Single Multi-label classifier or multiple single-label classifiers?

I have a problem to classify my data that can fit into more than one class at same time. Based on an initial study, I came across "Multi-label classifier" that can classify data into more than one ...
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240 views

binomial test for testing significance of classification very sensitive to hits count

I'm doing a multi-class classification task and I wonder if I'm doing the binomial test correctly since it is very sensitive to the count of correctly classified trials (hits count). Say, there are ...
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433 views

Modeling spatial correlations with multilevel data

I have ~5M records of spatial polar coordinates (r and theta). These 5M records are from 400 different 'locations' that I have ~150 variables for (no NAs). I also have 1 binary variable used to flag ...
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One-against-all probability values into a multiple class probability value?

I have a 10-class classification problem. I've approached the problem as a set of one-against-all binary problems. For each class I've built a MLP neural network that provides a probability estimate [...
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502 views

Classification accuracy is better with noise added?

I'm making a system to classify 2 class motor-imagery EEG of human brain. I use simple Low Pass Filter to pre-process the data (with or without added noise). I'm using LDA as my classifier. To test ...
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Document classification on Twitter: LDA or something non-generative?

On Twitter, I would like to assign weight to users based on how similar their bios are to a topic (e.g. technology would be a topic, the training set would be the bios of users who are known to be ...
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Given two dependent sequences, predict next output for one of them

I have a problem which can be generalized as follows. There is some black-box which produces actions {0, 1}. Having two sequences: X - series of previous actions, T - series of time intervals between ...
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Why do I get 100% error rate in unsupervised random forest, and how do unsupervised patterns work in “randomForest” R package

I tried to use random forest to classify microarray data. Basing on research of L.Breiman and Tao Shi, I constructed a synthetic data base using bootstrap methods (Assuming it is a matrix with samples ...
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What's a good range of weights to evaluate for $L_2$ regularized logistic regression?

I want to find a weight that minimizes an averaged cross validated misclassification score from an $L_2$ logistic regression classifier. Obviously, the search space for the weights should be bounded ...
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Asymptotics of 0-1 classification loss

I am interested in training a simple binary linear classifier. That is, I will find a vector of weights $\bf w$ such that I can predict the class of new example by the sign of $f(x) = w^T x$. I ...
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Generative modelling: what if the generating models have very different “quality of fit”

Say I want to classify my data into two categories. I am pretty sure that my data has been generated by two mixtures of Gaussians -- on has a bimodal and one a trimodal form. I then train the ...
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Do classification trees need to consider the correlation between attributes?

In decision tree classification, we use the attribute that splits records, like entropy, as split nodes. Does it need to consider the correlation between attributes?
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What are the signs of noisy labels in a dataset?

When learning a classification model in supervised machine learning, how can we test whether the labels in the dataset are noisy or not? Is there any particular way to check it or any specific sign to ...
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Does low frequent items require data preparation as high freq items?

I am learning analytics online and have some quick questions. Usually when we do analysis, why is that we usually ignore the items/data points that are less frequent? Let's say for ex: we have a ...
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How to prepare the training data for Support Vector Machine?

I'm currently doing some comparison of Naive Bayes Algorithm and Support Vector Machine classifying news to see each algorithm's accuracy. I already know how to prepare the training data for Naive ...
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Do imbalanced categorical predictors do any harm when classifying?

Assume I want to do some, say, churn analysis on a dataset. Decision Trees, for instance, are relatively robust to skewed distributions in the (numerical) features, but rather poor on imbalanced ...
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What is a better way to inference on chained machine learning models?

I'm trying to figure out a better way to make inference for my audio gender classification models. I have created 2 models: (1) model1 predicts adult/child, (2) <...
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Literary author classification from books content

Some background: I'm pretty much a newbie in NLP and in machine learning in general, I'm currently following some courses in my university about these topics. I'm working on my first ml project using ...
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What is the error for ImageNet “Object localization” challenge?

I have been reading some papers which use the ImageNet-LOC (ImageNet-Localization) dataset. I tried to read up on it to understand what the goal of this dataset is, and hence, what the error we are ...
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Similarity between clusters/groups?

I have a dataset consisting of multiple groups in a high dimensional space. An example is shown below: What would be the best way to calculate similarities between groups. Say how similar is group A ...
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35 views

Classification ML for sports betting

I'm trying to model the problem on my own and I just want to have feedback if I'm on the right track. Suppose I want to build a model that outputs the decision rule for the outcomes of a football (...
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How do I change my neural network from a classification task to a regression task?

I currently have a neural network that is doing a reasonable job in classifying an image into a number of classes although not that great. These classes however are essentially buckets on a floating ...
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1answer
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Can I learn binary classification and linear regression in the same network?

I would like to train a neural network on an input signal, and have it learn several unrelated decisions simultaneously (performing binary classification, one-class classification, and linear ...
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1answer
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Is the following considered an image classification or an object detection problem?

I've been assigned with the task of creating a model to detect whether and advertisement exists in an image and optionally to draw a bounding box around it. My first thought was that this is an ...
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Binary classification where I know only one candidate can be positive

I have a binary classification problem, where given a thing I need to determine whether it's of class A or class B. Now, I also have additional information: For each 30 examples for which I need to ...
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Gaussian Process for Classification: How to do predictions using MCMC methods

Problem I was reading about Gaussian Processes for regression in the "Gaussian Processes for Classification" textbook and in a few other online resources. Everywhere I look people seem to avoid ...
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How to choose operation point from precision recall curves for multi-label classification

Is there a commonly accepted method for selecting an operating point for a multilabel classifier to optimize for each of these aggregate metrics: micro averaged recall at some minimal acceptable ...
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1answer
91 views

how to avoid overfittig with xgboost and how to increase accuracy

I am doing a binary classification problem, I got to train 85% accuracy, but test accuracy is 72%, I tried different parameters, Cross valid, But overfitting doesn't change, please help me on how to ...
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Why does training performance suffer when scaling a feature in logistic regression without regularization?

I am training a model using scikit-learn via logistic regression with no regularization applied. Scaling one of the features by factor 10^6 negatively impacts training performance. To my ...
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4answers
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Why does linear non-logistic regression work as a linear classifier? What classification error does it minimize?

Suppose the data has two attributes and a label -1 or 1. So, we have a three-column matrix $X$ (two attributes and a column of ones for convenience of working with matrix notation) and a column vector ...
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1answer
410 views

F1-Score in a multilabel classification paper: is macro, weighted or micro F1-used?

I read this paper on a multilabel classification task. The authors evaluate their models on F1-Score but the do not mention if this is the macro, micro or weighted F1-Score. They only mention: We ...
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Relation between multinomial logit model and multi-class neural network

I'm wondering how much differ in these two kinds of model when comes to predict discrete choice. Consider a data-set explanatory $\boldsymbol{x}$ and response $\boldsymbol{y}$ where $\boldsymbol{y} \...
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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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Combining conditional classifier probabilities

Context I have several document classifiers trying to predict the correct document type for a document. For a given file, each classifier outputs a list of the probabilities of each document type. I'...
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1answer
39 views

How to model the correlation between predictions in classification problems

In most of deep learning classification problem settings, the predictions $p(y|x)$ are often modeled to be independent, namely, $$p(y|x) = \prod_{i=1}^{N} p(y_i|x_i)$$ However, this assumption may ...
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285 views

RandomizedSearchCV - worse accuracy than standard parameters

I am currently training a text classification model to infer product category (198 different ones) from product names. After evaluating a few models I have decided to stick with a Random Forest (...
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How can I leverage a pairwise scatter plot to help choose a machine learning model/classifier to distinguish the categories of iris flower?

This is a visualization of the Iris data as a pairwise scatter plot. The goal is to learn to distinguish three different kinds of iris flower, called setosa, versicolor and virginica. We can extract ...
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1answer
34 views

Logistic regression with binomial independent variable

I have a table of observations, with three columns --- (a) class labels (can be 0 or 1), (b) counts of successes (out of a certain number of Bernoulli trials) and, (c) numbers of Bernoulli trials. I ...
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Variable selection, variable reduction, and handling sparsity for binary text classification

I am trying to do a binary text classification using support vector machine. I am wondering if I am doing it right and I'd like to look for some answers to the questions in mind. The following ...
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How to quantitatively measure the learning complexity in comparing regression and classification task?

For the case of same input and same output, we can map the relationship with regression. Meanwhile, we try it with multi-label classification, by masking the output with simple binary mask : when ...
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1answer
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In this concrete example of applying sklearn knn (with kd_tree) on Iris Data Set, how many partitions are there?

The k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the ...
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2answers
585 views

Why perceptron is linear classifier?

It is said that perceptron is linear classifier, but it has a non-linear activation function f = 1 if wx - b >= 0 and f = 0 otherwise If i will use some non-linear function on linear combination of ...
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How to aggregate calibration curves which were created in cross validation?

When looking into Scikit's CalibratedClassifierCV I noticed that the object needs to keep multiple calibrated classifiers in memory to average the results in real time. I understand that these ...
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318 views

How to improve accuracy of Decision tree in R studio

I am currently conducting a study on the predictive qualities of odds (Regarding Football/Soccer). I have odds from multiple bookies on each of the seasons and leagues within the study ( as below ). ...
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1answer
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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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Binary Classification for Customer Dataset without Customer ID

My dataset is a log of phone calls. Each row represents a customer interaction with attributes such as customer age, customer job, and interaction outcome ('buy' vs 'no buy'). EDIT: the interaction ...
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How can I “select the proper test sample size” to “achieve the best classification quality”?

Homework disclaimer. We were given 10k rows of sample training data. The task is to train some well-known classifiers (as listed below), test their performance and estimate the expected ...

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