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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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False Negative vs False Positive for Multiclass classification

Suppose I have three classes 1,2,3. And there's evaluation like below, where second element is false prediction where model predict class 3 while ground truth is 2. ...
Muhammad Ikhwan Perwira's user avatar
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Applying PCA Before Training Multiple SVM Binary Classifiers To Reduce Data

I am working on a project which has a goal to determine if a new sample is part of Class A or Class A'. I need multiple of those classifiers. I will have an SVM to classify between: ClassA - ClassA' ...
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Model training loss always converge to 1.35

I'm trying to create a multi-class classification model using RNNs. The input data has a sequence length of 90 and consists of 5 features, normalized to the [0,1] range. Here's the network ...
Mangi222's user avatar
1 vote
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Calibrating CatBoostClassifier produces worse results

I'm performing multiclass probability prediction using CatBoostClassifier on a dataset with ~4000 rows, 13 features, 4 target classes. Dataset has outliers, but it is balanced. For this task I'm using ...
primadonna's user avatar
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Hinge loss vs categorical_hinge loss in Keras [closed]

I was working on a project where I implemented a CNN model with hinge loss in Keras. The task is a multiclass classification where I try to classify sensor measurements into 6 classes. I'm aware that ...
Imene Charabi's user avatar
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Theoretical support for sample size exponential increase with dimensionality in nearest neighbour multi-class classification

In multi-class classification using nearest neighbour, I believe that as the dimension of the space increases, we need exponentially more samples to keep the classification error under a certain ...
orematasaburo's user avatar
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1 answer
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How to evaluate multi-class classifier on probability prediction task?

I have a balanced dataset where each object (song) has one of the four target class labels (mood of a song). Example: ID feature1 feture2 feature3 target_class 0 0.5 0.11 125 upbeat 1 0.23 0.75 136 ...
primadonna's user avatar
3 votes
3 answers
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One class never gets predicted, regardless of the model

I'm working on a classification problem from a dataset containing three classes, with proportions {"0":0.43, "1":0.25, "2":0.30}. ...
Mordechai's user avatar
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How to compare if multi-class outputs are different?

I am testing different multi-class (16 classes) classification models (ANN, DNN, DL, and so on) and the overall accuracy varies from 0.75 to 0.92. Despite that 0.92 is greater than 0.75, is there a ...
aldo_tapia's user avatar
3 votes
1 answer
39 views

Precision and recall in the case of null predictive power

With a multiclass classification problem, if it is perfectly predictive, the probability, $P(j|i)$, of predicting class $j$ for the real class $i$ is 1 when $j=1$ and 0 otherwhise. In this case the ...
arivero's user avatar
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Conceptual question on Recognition vs conformation

So, this is a conceptual question on the design of a model. I am getting started with ML and on a slight time crunch to get some results. My problem is not a classical recognition problem. It’s more ...
H Khalid's user avatar
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Making a multiclass classification problem binary during data preprocessing vs using a multiclass classifier

Sometimes you have a problem that presents as a multiclass problem in your data, but you only care about the result of a binary classification. E.g. you have a manufacturing process which can result ...
Grumpy's user avatar
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Is Brier score strictly proper in multi-label problems?

In problems where one of $3+$ categories can be observed and we prodict the probability of each category being observed, it is known that the Brier score is a strictly proper scoring rule that is ...
Dave's user avatar
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1 vote
1 answer
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Clustered data and multiclass classification with GPBoost

Is it possible to do multiclass classification using GPBoost? For example when we have 3 or more classes (e.g. specie A/ specie B/ specie C) from a clustered data set (e.g. several measurements over ...
Oscar's user avatar
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1 answer
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XGBoost Calibration for weighted loss function

I am currently using XGBoost (in R) to perform multiclass classification. I am using merror=eval_metric and my objective is <...
HeyCool08's user avatar
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1 answer
392 views

Do ROC curves require probabilities?

In the binary case, the implementation of ROC curve in torchmetrics automatically applies a sigmoid when it detects logit inputs (i.e. when the values of scores are ...
Alex Bogatskiy's user avatar
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Should we increase the number of samples when adding more classes?

Assume we are solving a $k$-class classification problem, $k \geq 2$, and we have a trained classifier $\phi$ from a family of generative or discriminative classifiers $\Phi$ minimizing an objective $\...
Sanjar Adilov's user avatar
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How to encode multiclass target variable?

I have a ML project for classifying news articles. In my dataset I have a target variable called "category", which represents type of the article, ("IT", "Science & Tech&...
CraZyCoDer's user avatar
1 vote
1 answer
144 views

Multiclass Classification using Binary Representation and Sigmoid Activation in Neural Network

I am currently working on a multiclass classification problem where I have categorical variables that I've encoded using binary representations as follows: ...
palash behra's user avatar
4 votes
4 answers
742 views

Using regression where the ultimate goal is classification

Some background information about what I am trying to model before asking my question: Long periods of hot temperatures and dry conditions (heatwaves) puts pressure on the components in the power ...
Sam Malek's user avatar
2 votes
2 answers
91 views

Ideal scoring rules for multitask classification?

I am seeking advice for the best way to score a multi-output/multitask classification model's output. Problem setup A simplified version of the model is as follows: Training data have F features, say ...
FidusAchates's user avatar
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Metrics for multiclass classification

Background Consider a clustering problem where a dataset of measurements $Z\triangleq\{z_i\}_{i=1}^m$ must be partioned in $n$ clusters, where $n$ is unknown and must be estimated. Here the term "...
matteogost's user avatar
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1 answer
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Hierarchical labelling for independent variable in training data

I have data where the objective is to use supervised learning to predict 4 different outcomes. Say the classes are 1, 2, 3, 4. Though discrete, they are also hierarchical, where class 2 is of an ...
Mikee's user avatar
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1 vote
1 answer
65 views

Multilabel classification problem

I have a problem statement where I have two dataset one labeled where a data point can belong to only one class say class1 or class2 and there's another unlabeled dataset. Now for unlabeled dataset I ...
Sujit Kumar's user avatar
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1 answer
75 views

Multilabel Classification Task using SVM

I want to classify diabetic retinopathy grades (normal, mild, moderate, severe, PDR) using SVM. But the problem is i don't know which type of svm should i use, because i extract three lession features ...
anastasia's user avatar
2 votes
1 answer
265 views

Customized F1-Score for multi-class classification

Let's consider a multi-class classification problem with 4 classes: 0, 1, 2, and 3 F1-Score 'macro'-averaged is calculated like that: ...
AngelMarcos's user avatar
2 votes
0 answers
33 views

Repeating Target Values in output [closed]

I have created a multiclass predictive model, however, the target values are repeating. Why one of the target value is repeating and how can it removed? The code for this is as follows: ...
Akshita's user avatar
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2 votes
0 answers
51 views

Is there a multi-class classification model which can incorporate partly known conditional probabilities of the targets?

I am faced with a classification problem where I wish to predict 2 binary variables, say x and y. x and y are dependent in the sense that one conditional probability vanishes: P(y=1 | x=0) = 0. I am ...
Sebastian Chejniak's user avatar
1 vote
1 answer
75 views

Very balanced dataset and a multiclass classification problem, no context behind the inputs. Which evaluation metric to use?

I have constructed a simple neural network model, for a classification problem, with 10 target classes where an input (with some number of features) is to be classified to only one of the 10 classes. ...
creamedcheese83's user avatar
4 votes
1 answer
1k views

How to compare labels from clustering analysis and original ones?

I was asked to run a clustering analysis to assess the validity of labels for a manually labelled dataset. I can simply save the actual labels (4 classes: 0, 1, 2, 3) and run clustering analysis (let'...
AngelMarcos's user avatar
1 vote
1 answer
974 views

Multi-Label Classification where each label is a Multi-class problem

Problem: Currently, I have 15 classification models(multi-class + binary). Training and Maintaining 15 models take a huge time and cost. Also, I need to inference 15 models for every input. So I ...
Naren Babu R's user avatar
2 votes
0 answers
82 views

Calculating 95% confidence interval for multi-class predictions

I have a multi-class classification model that predicts the following probabilities for 4 classes in 3 test cases: ...
dokondr's user avatar
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1 answer
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R SVM classification with kernlab and user-defined kernel

I am trying to perform multi-class classification using SVMs (C-SVC). I am using the ksvm function from the kernlab package in R....
HeyCool08's user avatar
1 vote
2 answers
107 views

How to cluster and visualise vectors of which the components are class indices?

Let's say I have a dataset $\boldsymbol{\mathcal{X}}$ of $N$ samples wherein each sample $\boldsymbol{x}^{(i)}\in \mathcal{X}$, $i \in {1 \ldots N}$, is described by a set of $D$ features, such that $\...
Damiaan Reijnaers's user avatar
2 votes
0 answers
395 views

Does scikit-learn support plotting calibration curve for multiclass classifier?

I have trained a multi class classifier and calibrated the classified using scikit-learn library's CalibratedClassifierCV class. To check how well the probabilities are calibrated, I tried plotting ...
yodasoda18's user avatar
1 vote
1 answer
43 views

Incorporate known class likelihood (proportions, ratios, etc..) in the classification output

I'm working on the multi-class prediction problem, with 6 output classes. These represent different types of land cover. The classification model is pixel-based and I have extracted different ...
kap.provalija's user avatar
1 vote
1 answer
374 views

Can XGBoost do classification based on linear combinations?

Suppose we have a data set $\mathcal{D}$ consisting of $n_C$ continuous features $\boldsymbol{X}_1, \boldsymbol{X}_2, \dots, \boldsymbol{X}_{n_C}$ and we wish to target a discrete variable $\...
HeyCool08's user avatar
3 votes
1 answer
2k views

LightGBM interpretation of monotonic constraints in multiclass classification

When using LightGBM in classification problems it is possible to use monotonic constraints. In binary classification problems the interpretation is straightforward: "The probability of class (say)...
BLaursen's user avatar
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1 vote
1 answer
216 views

How to handle with long-tail classification

I have a long tailed distribution with many classes, and the num of samples per class is ...
Cranjis's user avatar
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1 vote
0 answers
45 views

3 classes imbalanced and hierarchical classification

I need to classift a dataset with 3 classes: A - 85% of the data B - 10% of the data C - 5% of the data Where C is a subset of B. What should be the best way to approach it?
Cranjis's user avatar
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0 answers
51 views

How do I know which class has been difficult to learn for my multi-class model?

For a multi-class model, there are always chances that the model is learning one class's features more than the other. But how do I find which class has been weakly learned? Please help.
shey's user avatar
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1 vote
1 answer
86 views

Linear Classifiers -- Single Linear Layer with k Neurons vs 'one vs rest' (k Linear Layers with 1 Neuron)

When using a linear classifier for a k-class classification problem, is there actually any difference of using a single linear layer with ...
keezar's user avatar
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2 votes
0 answers
231 views

How to assess calibration of probability distribution for a multiclass model?

I have a multiclass classifier (boosting model), and my goal is to have a good approximation of the actual distribution to the classes given my feature values. I.e. suppose I have features $X$, and ...
Lys's user avatar
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1 vote
0 answers
142 views

Multiclass ROC curve about one vs one

I am learning ROC about multiclass problem. I read this article https://towardsdatascience.com/multiclass-classification-evaluation-with-roc-curves-and-roc-auc-294fd4617e3a . I am confused about ovo ...
李宜倫's user avatar
4 votes
2 answers
193 views

Prediction of 'other' class

I'm training a MLP classifier with a softmax output that outputs 4 classes. For my particular application I'd like the classifier to output a fifth 'other' class when the input don't belong to any of ...
nico's user avatar
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1 vote
1 answer
569 views

Why does SKLearn's Logistic Regression model have the same coefficients as my own model for 1 class but have different coefficients for other classes

I am currently implementing logistic regression from scratch and I'm comparing my model with SKLearn's logistic regression. Since this is just an exercise, I decided to use toy data, specifically ...
Jed's user avatar
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1 vote
1 answer
33 views

What is the benefit from going from unordered to oredered data?

Let us assume that we have naturally ordered data that we want to classify. Then we can use ordinal regression/classification methods. Yet we can treat those as unordered and use multiclass ...
Hubert Drążkowski's user avatar
1 vote
0 answers
37 views

Classifying dataset with different number of features

I have a dataset like below: samplename position reference alternative S1 201 C T S1 3567 A G S1 760 T C S2 356 C T S2 6787 T C These data belongs to patients and ...
Nmgh's user avatar
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0 votes
0 answers
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How do i design my CNN so that i picks up on details like a logo [duplicate]

i'm quite new to machine learning, so i hope i can get som help on here. I try to classify pictures of soda-cans and bottles with size 299x299. So based on the shape of the object itself, my networks ...
kirkegaard's user avatar
0 votes
1 answer
65 views

Ways to detect noise in multi-class classification training data using text embeddings (BERT)

So I have a dataset with a column of text and and labels (5 different labels) associated with it. The labels describe the potential answer to the type of question being asked in the text column. For ...
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