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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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Does it make sense to mention F1 score on multiclassification problem?

I have two questions that came to my mind while reading some comments on a multiclassification problem: I read that models were evaluated using F1 score (which is a combination of Precision and ...
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Which loss function is the best for muticlass ordinal model?

I have a deep-learning model, where you can predict 5 different class (0-4). I want a model that punishes predictions that are more wrong. So if the model predicts 3 and it is a 4, it's not as bad as ...
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The effect of an imbalanced dataset on multi-class log loss in an imbalanced population

I have sampled data and labeled it as being 1 of 14 classes. This dataset is very imbalanced, e.g. I have a lot of samples for class 1 and not that many for class 14. However, this same imbalance is ...
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Is there any model that can handle time ordered multi classification?

I'm working on a little project and the task's the following: There are transactions for 16 products and ~75.000 clients on a 18-month time series, where each row is one transaction from one client-...
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Is it possible to identify which features contribute to the precision and recall for each label in sklearn's classification report output?

Below is an example of classification report output by sklearn. Is there an easy way to identify which features contribute to the precision and recall improvement for each label? I can start with ...
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Text MultiClass Classification - How many labels needed

I have a dataset similar in build to a product - category list with 5 categories and 2000 labelled products. I am new to ML but was wondering what the best approach for classification with limited ...
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How to report a confidence score of a multiclass classifier?

If we have a model which outputs class probabilities for $K$ classes, e.g. a NN with softmax layer, how can we return an aggregate "confidence metric"? Some intuitive ideas would be the ...
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261 views

Precision-Recall Curve Intuition for Multi-Class Classification

I am running a CNN image multi-class classification model with Keras/Tensorflow and have established about a 90% overall accuracy with my best model trial. I have 10 unique classes I am trying to ...
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What is a good model for heavy multiclass problem and small number of (textual) features? [closed]

I have a data set with a small number of textual features (less than 10) and a response variable with a lot of classes (~100). Is there any recommendation for models that are powerful for this kind of ...
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45 views

Random forest hyperparameter to control misclassification

May I know what hyperparameter to tune for random forest classifier to control misclassification? I'm doing a 5-class classification problem and it turns out that most classes are been misclassified ...
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SVM predicts always the same class

I have a dataset with tf-idf values and their corresponding classes and I am trying to do predictions using SVM. The problem is that all the results that it produces have the same class. Most related ...
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DL model for solving a multi-class classification problem works fine for the first class and then the performance gradually drops for other classes

I designed an AE shaped deep neural network to perform a multi-label class classification. The classes are not mutually exclusive. The last layer has n neurons; one responsible for each class. I pass ...
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multi-label classification in keras with huge number of classes

I have a training data of shape (100000,1200) and associated classes of size 3000. i.e, each sample has 1200 features and has to be mapped to one of the 3000 classes. How many hidden layers are ...

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