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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384 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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232 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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432 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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143 views

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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485 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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162 views

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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1k views

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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80 views

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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182 views

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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56 views

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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1answer
1k views

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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36 views
+100

How to create training data for CNN using remote sensing imagery

Before I start with the issue I would like to touch base with some background information. I had been working with Random Forest for classification of Remote Sensing data, here the classification ...
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4answers
54 views

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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16 views

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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32 views

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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11 views

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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44 views

Evaluating classification results when importance of correct classification varies with class

Suppose we have a categorical variable $Y$ and we are trying to classify it. Our decision (the predicted class for $Y$) is $\hat Y$. We are facing a loss function which can be represented by a loss ...
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1answer
26 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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13 views

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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11 views

Models for ranking possible classifications by confidence

What are good means of finding the various highest confidences or likelihoods for a (say) hundred possible outcomes of a classification problem? Inputs belong to only one class, unlike document ...
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2answers
79 views

What is the best way to get the most accurate results with this small dataset?

This is my first question here, I apologize if this is the wrong place or my formatting is not correct. My experience with machine learning and data science in general is a graduate level survey ...
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48 views

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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236 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
353 views

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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1answer
248 views

How does feature selection work for non linear models?

A model like a neural network or an SVM is called for only if the interactions between the features and the target is non-linear, otherwise we're better off using linear or logistic regression. But ...
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32 views

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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33 views

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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104 views

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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54 views

What is the number of filter when using CNN for sentence classification

I am new to machine learning and NLP. During reading convolutional neural networks for sentence classification I'm having trouble understanding it. In the paper it says that a feature map c has ...
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48 views

Combining multiple observation weights for classification

Let's say you have multiple sources of observation weights for a dataset. For example, you have a $[0,1]$ weight coming from the label's certainty ($w_c$) and another one coming from its recency ($w_t$...
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73 views

Skewness Impact on Classification

I have a dataset with 134 attributes and my goal is to build a binary classification model. While exploring the dataset, I found that there was high skewness present in the attributes. I wanted to ...
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44 views

Calibrating probabilities of a binary classifier when class prior is unknown

Is it possible to calibrate the probabilities of a binary classifier when the class priors are unknown? In cases where the data is obtained with selection bias (i.e. more positives than negatives in ...
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98 views

Are latent variable models the same as latent source models?

I am interested in the research done in this thesis and accompanying paper. This research discusses models termed latent source models. I have never heard of this specific term and the papers don't ...
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351 views

Clustering as a method to find and label classes for supervised learning

I'm working on a text classification project. We have around 300k documents (small, 1~2 phrases) and we don't know the set of labels or how many labels there are. The recommended approach to me was ...
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26 views

SVM classifier: strange location of support vector

I am playing around with Matlab's example which involves classifying whether data lie inside a circle of radius 1 (label: -1) or out of it (label: 1). I decided to experiment with things and flipped ...
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2answers
168 views

improving classification accuracy of the dataset as a whole by considering classifier distributions

Overview I'm new to machine learning so apologies if I misuse terms. I have an idea to improve my classification analysis that I feel is not terribly unique, but I can not find a reference to such a ...
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36 views

Boosted decision trees: in which situations are “deep” decision trees performing better?

The general idea of boosted decision trees is to use very simple trees in the following manner (simplified, for intuition only): start with a simple tree, fit another simple tree on the residuals, ...
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134 views

Optimism bootstrap with non-linear models

I have come across an example in my research with heavily overfit non-linear probabilistic classifiers, where the optimism bootstrap appears to underestimate the optimism, even when using a proper ...
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178 views

VC dimension of rectangles in 2D space

I understand that the VC dimension of axis-aligned rectangles is 4 because there exists a set of 4 points that can be shattered by a rectangle and any set of 5 points cannot be shattered by a ...
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40 views

Unbalanced data on fire for a binary classifier

I have a lot of training data from which I want to build a binary classifier, but the classes are highly unbalanced, 97% in one class, 3% in the other (even though, in absolute terms, I still have a ...
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46 views

SVM optimization problem with constraint

I am studying SVM from Andrew ng machine learning notes. I don't fully understand the optimization problem for svm that is stated in the notes. So we have optimization problem $$\max_{\gamma, w, b}\...
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1answer
453 views

How to manually balance unbalanced multi-class/multi-label data?

I have a multi-class and multi-label classification problem, i.e.: each sample can have more than one label associated to it and there is a total number of M ...
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1answer
25 views

Classifying compositional vectors of time series

I am interested in classifying vectors of time series $x_t=(x_{1,t},\ldots,x_{n,t})$. In addition these vectors are subject to the restrictions $\forall i,t$: $0 \leq x_{i,t} \leq 1$ and $\forall t$: $...
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1answer
219 views

classification on imbalanced dataset via random forest: results vary with random seed

I have a highly imbalanced dataset of about 8000 observations, with 11 features and one binary target variable. I want to predict the target labels, considering that the "1" target label occurs for 1....
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2answers
135 views

How to choose a method for binary classifier based on only positive and unlabelled examples?

I need to build a binary classifier with machine learning, as I fail to manually choose a combination of features to achieve minimal fraction of false positives. What is best practice for choosing a ...
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1answer
655 views

Tf-idf for text classification: On what should IDF be calculated?

The TF-IDF value of a word specifies how important a word for each document is. My setting is any text classification where one has multiple documents of with different classes: Let's take a lot of ...
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0answers
127 views

Similarity between Train and Test data sets

I have multiclass classification dataset and I am using Deep nets for the classification task. To explain the problem, let's assume that I have 5 classes to classify. No matter what I try, be it ...
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3answers
590 views

Longitudinal panel data classification

My problem context specifically lies in churn modeling, where accounts have account-specific attributes (like industry, number of employees, etc), but also have longitudinal yearly data (product usage ...
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35 views

comparing CNN vs other classification methods

I working on a classification problem. I have created Python code that takes certain labelled input data. This is then converted into two 2 dimensional arrays. The first array is an input array of ...
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96 views

What is the definition of margin for multi-class classification?

I heard the definition was as follows: Let $y_{best} = arg \max_{c \in Classes} f(x)_c$ be the best class and let the prediction function be an output vector $f(x) \in R^{|Classes|}$. Then define: $$...