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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How to estimate a Class Probability Prediction Interval for Random Forests?

I have found multiple questions here (e.g. this) and great academic papers (e.g. this and this) about calculating prediction intervals for Random Forest and other techniques applied to regression ...
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Why does multi-class classification using libsvm show a linear trend in the predicted categories?

I am adapting MATLAB code from a colleague's SVM pipeline to do eight-way classification on sets of EEG data, i.e. one prediction is made at each time point across the data, with each channel's ...
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How can I evaluate my multiclassification model using cumulative gain?

I want to run a model for multiclassification problem and I am only interested in the top x% results (recommendation model). I think using the ndcg@1000 evaluation metric is the best for this purpose, ...
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Ranking prediction model by keyword

I have to predict rank(1~100) of online sales products. There are 'keyword' and 'date' columns, so each keyword and each date, there are 1~100 rank. And for X, there are 20 variables(review number, ...
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hyperparameter search with unknown test set distribution

I'm training a 3-class neural network classifier (conv layers and softmax at the end, nothing special). Let's say, in the test set I will have N1 examples of the 1st class, N2 examples of the 2nd ...
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Multi-Multi-Class Classification

I'd like to build a model that can output results for several multi-class classification problems at once. Suppose you have diagnostic data about a product that needs to be repaired and you want to ...
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What can I do when Overfitting doesn't seem to go away by any means?

So first of all I've seen a lot of overfitting questions around here, but none of the answers seem to improve my model. I wrote a neural network made without frameworks (only used numpy), and for the ...
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4 views

Decision function to draw conclusion from two separate models

So I have trained two separate classifiers using sklearn's built-in Gradient Boosting Classifier. One of the classifier is responsible for classifying four classes(0, 1, 4, 6) while the other one is ...
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20 views

Increasing precision for one label in multiclass classification

I am doing multiclass classification for 3 labell with neural net. The model works fine but when I check precision/recall per label in validation set I can see that precision is a little bit too low ...
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Is this scenario a multi-task learning or a multi-label classification?

I would like to predict the Vehicle Descriptor Section (VDS) of Vehicle Identification Number (VIN) based on features like vehicle year, make, model, engine size, body type, etc. Expected output: A 5 ...
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Categorical cross-entropy vs Binary cross-entropy for multi-class classification with mixup

I understand that for multi-class classification the correct loss to use is categorical cross-entropy. However, when performing mixup as a regularisation technique two samples $(X_1, y_1)$ and $(X_2, ...
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15 views

How to interpret high and similar ROC-AUC across models with slightly more marked differences in prAUC? (multiclass classification)

I am trying to compare the performance of different models for a multiclass classification task. The dataset itself has about 50 different categories with a lot of imbalance (the cardinalities of the ...
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SMOTEBoost implementation

This question got to do with SMOTEBoost implementation found here but I believe the issue is relayed to imblearn library. I ...
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compare multi-classification models with different target length

I have a given classification task where I want to classify text based on top, and I also have a taxonomy of topics that looks like this: ...
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Multiclass classification vs Binary classification with class merging: prediction accuracy

I have a dataset with 4 labels. For me the most important is to be able to distinguish label 1 from all other labels, I don't care that much about distinguishing between labels 2,3 and 4. The ...
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52 views

Multiclassification: precision-recall from scratch vs sklearn

I would like to know if there´s any issue behind using sklearn's precision/recall metric functions and coding up from scratch in ...
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Classification Problem using multiclass features input and ensemble methods

I am working on a classification problem. I am applying tree-ensemble methods (Histogram-Based Gradient Boosting and Random Forest) and evaluating premutation importance in order to understand ...
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Improve Multiclass Classification by Binning weak classes?

I have a imbalanced Dataset with 23 classes from Accounting Data. My goal is to provide a suggestion to the accountant, which Account a Transaction belongs to. Gradient Boosting and any other Ensemble ...
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MultiClass Classification - Training OvO and OvA

I like to know how OvO (One vs One) and OvA (One vs All) models are trained in multiclass classification problem. To keep it simple, we have 4 classes, each of which has 1000 datapoints. What are the ...
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Can Transformer neural networks be used for botnet attack classification?

I would like to ask for some advice/guidance regarding a deep learning project we're working on, we're trying to do Feature analysis of IoT botnet attacks using Deep Learning we're working with the Nb-...
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39 views

Weighting the loss function based on previous seen true positive rates

Similiar to class imbalance there is always something I would call "learnability imbalance" in multi-class classification. What I mean by that: Even when the classes are evenly distributed ...
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1answer
75 views

Accuracy always equal to recall

Fitting 3 different models on a 5-class imbalanced dataset. The results show model accuracy always being equal to the recall. How can this be possible? ...
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37 views

Treating an inherently multiclass problem as a two-class problem

I'm not an expert in this area, so if this question sounds a bit ill-informed, it is! I'm working on a problem somewhat akin to classifying birds with image data as input. Let's say for training data, ...
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Why am I getting good accuracy but low prediction with Logistic Regression/KNN (Multiclass problem)

I am currently trying to solve a classification problem using machine learning algorithms. Code: https://colab.research.google.com/drive/1mcgxVT1GifYbCYjfWyCm94Z2Y_bQHRhA?usp=sharing Datasets: https://...
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1answer
61 views

multiclass classification with weights vs competing risks with censored data

I want to fit a machine learning model to a dataset which is basically a survival analysis with competing risks with several failure types (e.g. mortality causes). However, I want optimal predictions ...
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1answer
30 views

Interpreting classification metrics for multiclass imbalance

I am at the point of reporting my results in a research article conducted. The dataset is highly imbalanced with class 1 and ...
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Problem with a dataset not being properly labelled

I have a labelled dataset but these classes are not perfect. Some classes should be combined into one, whilst others have too few data-points for training. My main concern is the former not the latter....
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52 views

word embedding using Keras Embedding layer

I am learning using Keras Embedding layer to build embedding models. However, I failed to build a good embedding model. Can anyone help me check where I did wrong? Or not enough data to train? Data ...
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1answer
34 views

What is the maximum Target cardinality in multi-label classification?

I have a dataset that consists of a target column with 65 classes. Also, the dataset has 200 columns/features. I researched multi-label classification and found the popular algorithms that can be used ...
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1answer
109 views

Metrics for multiclass classification model accuracy

Usually the last layer in multiclass classification models is a softmax, which is essentially a vector with elements the confidences for each class. The standard top-1 accuracy takes account only if ...
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In multiclass classification, why do we have K but not (K-1) output units for softmax layer?

In binary classification, if we can transform the softmax function (needs 2 outputs) to sigmoid function (needs 1 output): $$\begin{align*}\mathrm{Pr}(Y=0|X)&=\frac{e^{b_0\cdot X}}{e^{b_0 \cdot X}+...
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19 views

Prediction Model for Naive Bayes Multi-Class Classifiers

I've been using Naive Bayes for multi-class classifications, but I'm curious what's actually happening mathematically. I have had difficulty finding a straightforward mathematical explanation online. ...
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1answer
113 views

Deep Neural Networks to combine regression and multi-class classification problems

I have a dataset obtained from a mobile app which is applicable for regression problem since the output values are numerical. I need to predict the numerical values and then predict their classes (...
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86 views

ROC AUC for multiclass problem

Just some quick questions to clarify my doubt please. I know that one can get precision/recall for each class in a multiclass problem, e.g. in this classification ...
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27 views

Discriminant analysis with only one independent variables in R

I have one categorical variable with 12 classes (t) and one independent continuous variable (x1). One of the classes of the categorical variable is the reference class. I want to apply discriminant ...
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9 views

One-vs-All ROC curve computation

Suppose you have a classification model which can predict any one of N classes. The model can also output the prior probabilities of that prediction, one for each class. These probabilities can be ...
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7 views

Many identical false negatives in multi-output classification

I used an XGB model to classify 12 categories (I call them classes 1-12). I've found that a particular false positive, 5, is predicted frequently for what should be '2', according to the actuals that ...
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1D CNN for multistep multiclass timeseries classification

Suppose you have a timeseries classification task with n_classes possible classes, and you want to output the probability of each class for every timestep (like ...
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Is creating artificial class imbalance in synthetic training data a good way to tackle hard cases in classification?

I have a problem where I need to classify something around 50 different classes. Some of the classes are very similar to one another and the algorithm tends to confuse them. However, I can create a ...
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1answer
45 views

Multiclass classification metrics : average probability

I'm looking for a metric to evaluate my classification model. I have 3 differents class (0,1,2). And I want to get the average probability of the good label. For example, if my ml model get me those ...
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8 views

Combining scores for different classes

I'm trying to classify samples into two classes, let's say cats and dogs. Each class also has subclasses, let's say cat/dog breeds. I'm trying to check whether training on more specific classes and ...
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50 views

Good probabilistic classifiers?

Are there any opinions/references for multiclass probabilistic classifiers that give 'good' probabilities, 'generally' speaking? By 'good' I mean well-calibrated, e.g. when it predicts a class as 70%,...
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1answer
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What is the best way to eliminate neutral words in a text classifier?

I'm creating a news classifier using the reuters dataset. Right now I'm in the process of preparing the dataset for training. First I removed all punctuation, numbers and special characters and after ...
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21 views

Random Forest behaviour with multi output hierarchical dependent variables

I have trained Random forest on multi-output(4) variables in python where each dependent variable is multi-class and variables have hierarchical dependency. I cannot provide the actual details due to ...
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2answers
32 views

Bernoulli Trials with Bayesian Analysis

Can Bayesian analysis be used with multi-class problems? For example, reliability analysis is usually concerned with "failed" or "not failed" classifications. My understanding is ...
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Good metric / method to evaluate balanced multiclass classification when some classes are more similar than others?

Surely I'm not the first person trying to do this, but can't find a good answer (probably because I'm not searching with the right terms). I have a problem with 10 balanced classes (0-9) where the ...
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27 views

Training a multi-label classifier neural network from a multi-class dataset

I have a multi-class (one-hot encoded) dataset with numerical and categorical features. I wish to to train a multi-label neural net classifier using this dataset. Here is a table depicting the setup: ...
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11 views

Use task covariance to reweight predictions from independent models

I have a $K$ complex nonlinear models, each of which makes a binary prediction $y_k$ according to a task specific feature encoding $φ_k(x)$. I know that certain tasks are correlated and some are anti-...
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53 views

Multi-class LDA (pairwise classification)

From this post: $ w=S_{W}^{-1}(μ1−μ2), $ is used to estimate $w_{0}=\frac{1}{2}(μ_{1}−μ_{2})^{T}S_{W}^{-1}(μ_{1}−μ_{2})−log(\frac{P1}{P2}),$ However, this is for a situation where there are only 2 ...
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224 views

Using FCNN for multi-class semantic segmentation trained on single class labeled image data

I am working on project where main task is semantic segmentation of land cover and another objects in Sentinel 2 multi-spectral images. Currently I posses dataset ...

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