Questions tagged [multi-class]

Multiclass classification is a classification task in which there are more than two classes. It is also called multinomial classification.

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Optimize classification rule in multinomial logistic regression

We know that in the case of logistic regression, a classification threshold p=0.5 is generally not an optimal choice when seeking to optimise sensitivity and specificity. This is generally due to the ...
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Does having the same background for all the images of a particular class increase a CNN model's ability to classify the images?

I'm working on a multi-class skin disease classification problem. The input images are skin diseased images that have varying backgrounds. Does maintaining the same background (maybe a white/black ...
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Does watermark/text on images at the same position influence the classification of images using CNN?

I'm working on a multi-class classification problem using CNN. Most of the images of each class have a text/watermark at a specific position on the image. I have a couple of questions. Does the ...
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What are some of the commonly used image processing techniques for multiclass image classification?

I'm working on multiclass skin disease image classification(caused by bacteria and fungus). Some of the sample images are shown below. Images contain different background as shown in image_1 and ...
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Low classification accuracy

I want to do a multi class classification with 6 classes. Whole dataset has 12750 and 56 features samples, so every class has 2125 samples. Before prediction I reduces amount of outliers by ...
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How to use time-series observations on multi-class classification problem?

I have a multi-class classification problem with time-series features. You can find an example series below. It shows the same series over time for different classes (actually, each line represents ...
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Difference between ROC-AUC and Multiclass AUC (MAUC)

I am trying to understand the interpretation of these metrics in a multiclass scenario: ROC-AUC and MAUC. Scikit-learn provides ...
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Prove the Bayes Rule for multi-class classification

Consider a classifier $\delta:\mathbb{R}^{d} \to (1, \ldots, N)$. Let the misclassification error by written as: $L(y,\delta(X)) = {\sf 1} (Y \neq \delta(X))$ where $X \in \mathbb{R}^{d}$ Prove that ...
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imbalanced data creates biased results in multinomial logistic regression, balancing it spreads probabilities almost equally - what can i do?

I am working in Python. I am using this method to generate the transition probability matrix of a Markov-Chain-Model. Each row of the Matrix is one multinomial regression that gives me the ...
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Too many categorical predictors in multinomial logistic regression

I am not familiar with multi-class prediction so I apologize in advance if this questions seem very basic. Here is my dataset: So within the dataset, I am trying to predict which fare product is ...
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How to predict both category and sub category in machine learning classification?

There are 4 actions available. Each action has its own varying number of categories. The target is to predict an action along with the category of action, given input data. Assume actions are a,b,c,d ...
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My training accuracy is 1.0 and my test accuracy is 0.994. Am I overfitting for a multiclass classification?

This is a multiclass classification for an imbalanced dataset. I set the class_weight for this model to "balanced". I have a perfect training accuracy (1.0) and a nearly perfect testing ...
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Generate simulated dataset for multi class classification

I want to generate multi class classification data in R. Suppose, I want to create 4 classes and $X|Y=y \sim \mathcal{N}(\mu_y,\Sigma_y)$. I know that we will use ...
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Generate multi class data from Simulations

I want to generate multi class classification data in R. Suppose, I want to create 4 classes and $X|Y=y \sim \mathcal{N}(\mu_y,\Sigma_y)$. I know that we will use mvrnorm function in R to create ...
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3 votes
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Machine learning for causal inference

I have a multiclass classification problem where the target variable is actually different categories of causes, and the dataset is observational. I know of causal inference, and I would like to learn ...
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How am I misunderstanding sklearn.metrics.roc_auc_score? [closed]

The documentation for scikit-learn sklearn.metrics.roc_auc_score() contains two statements regarding the 'average' parameter that, together, are confusing me: Note: multiclass ROC AUC currently only ...
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Why would including a feature reduce performance of Naive Bayes (multi class prediction)

I have a simple Naive Bayes model, 5 features, trying to classify observations into 1 of 3 classes. I add a 6th feature and the test set performance, from 58% correct classified to 54% correctly ...
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Does using LDA components in classification models lead to feature leakage

I have a multiclass classification problem with (too) many independent variables. And in order to reduce the number of features I did a Linear Discriminant Analysis (LDA) on my data, which created two ...
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Learning multi-label classification from single-label samples?

I am trying to design an online training regime for a neural network where have samples that can be any number of $k$ classes, but I only have access to samples with single labels. For example, if $k=...
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How the SVM algorithm works with one label in range?

I have a dataset where the features are configurations (numeric values) that describe the situation and the label (only one) is the ranking of the situation (natural value between $[1,5]$). If label ...
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Can I use Scott's pi (kappa unbiased) instead of Cohen's kappa for multi-class classification?

I am doing a 3-class time series classification task, where classes are 0, 1 and 2. I calculated the confusion matrix by using Python's PyCM library. I heard that kappa is the most important metrics ...
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Can I use Pearson's correlation coefficient or Spearman's rank correlation coefficient as metrics for multi-class time series classification?

I am doing a 3-class time series classification problem, where classes are 0,1 and 2. For example, the test set is ...
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ML generated word choice to create distinct "speakers" [closed]

How hard a project would it be to use ML to assist a single author/script writer in writing dialog where each "speaker" sounds like a distinct person? Is that something that a professional ...
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Determining if a model is efficient using a ROC curve or how does an OvR Classifier Know a False Positive?

I'm trying to predict if a change to a file will result in a bug in software development. I was recommended to create an ROC curve to evaluate how effective my model is. However, my last contact with ...
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2 votes
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Effects of class imbalance on neural network weights

My question is about unbalanced classes problem in case of a classifier neural network for natural language processing (in particular, a neural network with LSTM). I want to train a neural network to ...
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1 answer
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How to fully evaluate a multiclass classification problem?

When you have a multiclass classification problem, what is the right way to evaluate it's performance? What I usually do is to display the confusion matrix and the ...
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How to practically calculate the accuracy of each class in muliclass classification problem?

I have the following confusion matrix: ...
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Multi class classification using Neural net

I'm trying to write a neural network code from scratch using R. suppose i want to use this network for multi class classification with squared loss for performance function. (squared l2 norm of output ...
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Should I scale my data before a Cartesian-to-spherical conversion?

If I have three features, should I scale them before converting them to a spherical coordinate system? I have been working on a ternary classification problem. My data is high-dimensional, so I've ...
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Mean average precision varying based on classes

In my dataset, I have 10 classes, which have some peculiar characteristics (e.g., a single "object" can be separated into multiple disconnected parts). Now, the goal is to perform weakly ...
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Multi-class multi-label with partial mutual exclusivity

Given an input, I want to predict 0/1 for each of N output classes. The output can be 1 for multiple classes. So I'm training with individual binary cross-entropies for each of the output classes. ...
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One Feature to Rule Them All: Too Good to be True?

I am trying to build a ML model for diagnosing a certain group of several related but distinct diseases. Using a publicly available medical dataset, I performed data cleaning and feature extraction, ...
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Multi-class classification: I am applying an uncertainty threshold to send predictions to human, but I want to statistically determine its usefulness

I have the following prediction scenario: $labels\_true = [0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3]$ $predictions = [0,0,0,1,1,1,1,0,2,2,2,2,3,3,3,3]$ $uncertainty\_in\_prediction = [0.01, 0.01, 0.02, 0.1, 0, ...
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Problem Inverse transformation of MultiLabelBinarizer

I am doing a classification problem for that I was having my target column as encoded data, Like this: ...
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Difference between Hamming Loss, Hamming Score, and Hamming Distance in multiclass multilabel classification

I am trying to understand the mathematical difference between, Hamming distance, Hamming Loss and Hamming score. I am trying to perform two actions Multiclass multi label classification using SVM K ...
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191 views

under sampling a multi-label dataset

I have a multi-label dataset, whose label distribution looks something like this, with label on x-axis and number of rows it occurs in the dataset in y-axis. ...
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How to evaluate F1 performances using sklearn for imbalanced multiclass classifier, with different weighting for train and test sets?

I have an imbalanced dataset. The number of occurrences\weight of each class in the test and train sets is different. I wish to use the sklearn implementation of the F1 score for the evaluation. I am ...
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Looking for multilclass classifier that can handle repeated measures

I am looking for a multiclass classifier that can handle repeated measures. Specifically, each of my subjects appears multiple times with the same number of n classes. Now I would like to fit a ...
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4 votes
1 answer
574 views

How to draw ROC curve for a multi-class dataset?

I have a multi-class confusion matrix as below and would like to draw its associated ROC curve for one of its classes (e.g. class 1). I know the "one-VS-all others" theory should be used in ...
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Account for distances between target classes in loss calculation

I'm currently using categorical cross-entropy as the loss for a classifier that predicts cities given a number of features. One thing I'd like to try is to make the loss function aware of the ...
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connection between multi-label classification and multi-class classification

For a dataset with multi-label judgement, e.g., coco dataset but where we only want to predict the most-possible label. There're multiple ways, for example : 1) train as a multi-label learning(each ...
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Relation between R_k statistics vs MCC calculated per class

In case of multi-class classification I derive TP, TN, FP and ...
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Multiclass classification with variable output classes

The training data contains instances where only some (not all) classes are possible. Only one of the possible classes is the true class. How could one model this? I am having trouble finding any ...
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1 answer
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Can in theory a multiclass neural network classifier be seen as multiple binary neural network classifiers? [closed]

I would like to know more about the theoretical implications of such a statement.
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Given a multiclass classifier, calculate one threshold per class to maximize recall under precision constraint

Given a classifier $f$, $N$ possible classes, and an input $x$, $f$ produces a class from $[1,..., N]$ and its matching confidence $[0,...,100]$. Then I run $f$ on a large set of examples $X$, and I ...
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How to get multiple outputs using classification techniques?

I want to predict roles based on technical skills column.I have column technical skills for ...
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Which model to use for multiple outputs in classification problem

I want to predict roles based on name, experience, soft skills, technical skills . Based on all these variables I want to ...
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Unseen Classes in Decision Tree algorithms

I'm trying to train an XGBoost algorithm in a multiclass classification case, where the number of classes is very high (~6000). I recognize that this is quite difficult to do as it is, however, my ...
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Why Does using a OneVsRest Model for multiclass problem result in overall low accuracy but high accuracy for each individual class?

I have a multiclass problem i.e. 4 labels 0,1,2,3. I used a OneVsRest model wrapped around an xgboost model. What happens therefore is that i train a model 4x for each class. e.g.: ...
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how train AdaBoost M1 weak estimators?

I'm trying to implement AdaBoost.M1 as explained in Boosting: Foundations and Algorithms by Robert E. Schapire and Y. Freund. The problem is that I don't understand at each iteration t the estimator ...
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