Questions tagged [multiclass-classification]
Multi-class classification is when you have a classification problem with multiple classes, specifically 3 or more classes. Many classifications are binary by design, therefore the additional nomenclature of multi-class classification was defined to describe algorithms capable of classifying datasets with more than 2 classes.
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I used SMOTE-ENN to balance my dataset and it improved the performance metrics, but how can I be sure it's not overfitting?
The models were evaluated using 10-fold cross validation.
foldCount = StratifiedKFold(10, shuffle=True, random_state=1)
The models in question are XGBoost.
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Class Imbalance in Dataset of Images
When dealing with an imbalanced dataset, I have been taught to oversample on only the train samples and not the entire dataset to avoid overfitting, however this was for structured text based data in ...
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Weighting and loss function for multi-dimensional output on ECG neural network in Tensorflow
I am working on a DNN that is training on ecg data with a shape of [None,1,2500] and output shape of [None,12,19] where 19 is a ...
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Multiple classes present in one-hot encoding
When dealing with classification for multiple classes present in the same sample, can the output layer have the form of one-hot encoding, but instead of only one hot, have multiple?
That is, in case ...
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Is there a way to use CNN to separate/cluster the images into N clusters without online learning or only very mild online learning?
There are lots of examples to use CNN as classifier to separate images into known classes. There also lots of examples to use CNN as encoder and generate embedding to check the similarity of objects.
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What else can I do to help my model my classification task?
I have a classification task that I'm currently getting really low accuracy metrics on (my highest accuracy score is about 20%). So far I've run 5 models: quadratic disc analysis, logistic regression, ...
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What to do if my dataset have only One instance for class in classification?
I am working on a benchmark dataset for text classification. The dataset has about 300 classes, and approximately 50 of these classes have a single instance. In a paper that used fine-tuning BERT, the ...
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How to explain relative difference between macro-AUC and macro-F1 in a multiclass classification problem?
I recently published a paper in which the result of a supervised model is the following.
All the metrics are macro-averaged.
I have been asked to comment on the gap between the AUC and the other ...
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Movement in cohorts
I am working on a user sales data which gets updated week over week. Based on the sales done in each week, the user is categorized in segment A, B or C. This means size of each segment could change ...
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Unable to get explainability for sklearn Random Forest classifier using Shapash
I'm using SmartExplainer of shapash 1.6.0. I have 30 input features out of which 25 are categorical. I have preprocessing transformer object for the same. The code snippet is as follows
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Getting Error: TypeError: cross_entropy_loss(): argument 'target' (position 2) must be Tensor, not tuple
I am working on a CNN multi-class classification of different concentrations (10uM, 30uM, etc.) I create my dataset to include the images as the features and the concentrations as labels. Note that ...
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Data Preparation for next word prediction
In most places, I have seen that when preparing the training data and label for next-word prediction from the corpus one uses a fixed window size say of length 4, and then scans the subsequences of ...
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Multi-label classification inference
I am working on a multi-label classification with transformers. I want to assign tags to input text. First, I have trained a model multiclass and with the pipeline function I can retrieve all possible ...
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What loss function to use for predicting discrete output sequence given a discrete input sequence?
I am working on sequence-to-sequence tasks where the input is an n-length sequence of discrete values from a finite set S (say ...
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How to do train-test split for multi-class classification
I am performing multi-class classification problem of different concentrations of Acetaminophen in a specified dataset. My data is in the form of images and I am using CNN. I have compiled all the ...
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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'...
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How to deal with ambiguous classification outputs that exceed the specified threshold but are too close together?
I have a simple classification setup (intent classification). Once an input is received it's parsed using Multinomial Logistic Regression and then a score is predicted for each class. I pick the ...
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What statistical model suits for this problem?
I have a dataset with 6 target variables and the target variables are Boolean. The requirement is to use logistic regression to build the model. Which ML approach can be used in this situation?
Will ...
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Do I need to use always the same "Test" dataset to compare between different models?
I have two datasources A and B, and I want to check how several methods can affect the accuracy of my multi class models:
If I use cross-validation with validate dataset to obtain the best
hyper ...
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DecisionTreeClassifier cannot take one-hot encoded classes?
I got ValueError: Found array with dim 3. None expected <= 2. I dont know which array has dim 3?
DecisionTreeClassifier cannot take one-hot encoded classes?
But ...
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WARNING:tensorflow:Your input ran out of data; interrupting training Error
Due to VRAM capacity limitations, I cannot fit the whole training and validation data into the GPU memory. Instead of cutting some of the data out, I decided to use TF.dataset object to create the ...
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How to get the top feature contributors for the differnt classes in the classifiction model?
In classification model , we build models with binary/multi class responses. Is there way to get the top features contributing positively,negatively to each of the classes.( i.e top features helping ...
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error useing soft max gives outputs greater than 1
I am using Hugging Face AutoModelForSequenceClassification, model is roberta, using it for text classification.
There are 3 classes.
The output is: ...
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Multi-class classification model unable to return desired outcome
I have a scenario of multi-class dataset with around 10 distinct classes of target. There are 3 categorical features each with multiple labels. If we check the data, each unique combination of feature ...
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LSTM model is producing really bad results for multiclass text classification for imbalanced small dataset
I am training a LSTM model on my current dataset to predict the multiclass categories - there are 18 mutually exclusive categories and the dataset has ...
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If I'm comparing performance between two different datasets should sample and class size be uniform?
If I'm comparing performance between classification models on two different datasets should the number of samples per class, the number of classes, and features per sample be the relatively the same ...
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Predicting many classes, is it a known solution to build n-group classifiers?
Imagine you want to predict 2048 classes. Instead of asking one model to predict all of them at once, is it a known type of solution to have a model predict which cluster or group of classes an input ...
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Get dependant probabilities in multiclassification
After training my CatBoostClassifier model I call get_proba function which returns me list of probabilities. The problem starts from an another point... I transfer that data into dataframe then to ...
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The best approach and library for time-series similarity
I have a time-series classification problem with IoT signals. The training dataset has seven target signals.
I used tsai as a fastai/torch library, and I achieved satisfying results.
However, in a ...
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Predicting multiple categorical targets: Single model vs multiple models
I have to predict multiple categorical columns. Each one is a multi-class classification.
Should I train separate models to predict each column individually or create a new categorical column which ...
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Is there a procedure for determining if a classification problem is ill-defined?
Consider a group of objects denoted $O = \{o_0, o_1, \cdots\}$ where each object is associated with a feature vector $F = \{f_0, f_1, \cdots\, f_{N-1}\}$. For this case, assume the features are ...
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Different parameter estimates for multinomial regression vs multiple logistic regression models
I am new to multinomial regression but looking online I gathered that if there are k=three outcomes I can solve the problem using k-1=2 logistic regression models where:
model 1: $ln(\frac{P(y=group2)}...
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improve accuracy for LinearSVC (multiclass text classification)
I'm working on a project in which I'm trying to classify bugs (taken from Jira) to their relevant assignee group.
After creating and cleaning the dataset (~50000 records), my best results have always ...
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How to interpret the Shapley values returned by TreeShap for multiclass classification?
I have read in Molnar (2022) and Gianfagna (2021) books that the TreeShap method returns the exact Shapley values of Shapley (1951).
The Shapley value estimates, given the current set of feature ...
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How to split train/test datasets according to labels' classes
I faced a problem while I using sklearn.train_test_split().
Here is the code I use.
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VIF - Variance inflation factor in random forest classification
can i use VIF for random forest classification. My task in hand is multiclass classification. I read it somewhere that you can use VIF only for regression Task. Is it true?
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R tabnet package (by torch) need an example of multiclass classification
I'm trying to train a model with three classes but I'm getting terrible results, can anyone show me what I'm doing wrong?
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Can/should a logical multi-class classification anomaly detection system be described as "unsupervised machine learning"?
I would like to ensure that my use of terminology is accurate. My question is: what terminology should I be using in this case?
The system I am building assigns classes (-1, 0, +1) to observations ...
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Adaboost multi class classification
How do I use adaboost for multiclass classification? Do I split the classes into two groups and categories the groups as 1 and 0
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looking for datasets to build multi-output models
I am looking for beginner friendly datasets(of any type) that can be used to train a deep neural network with multiple outputs. I tried looking on places like Kaggle but there is so many datasets that ...
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Trying to find a Model for multiclass classification problem where you want two classes close together but far away from a third?
So I've got this problem where I want to find out where two classes are more a like ( features wise) than a third. So for example if I have three classes {A,B,C} I want to find out where class A and B ...
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Accuracy decreases after adding more samples
I'm working on a multiclass text classification task (5 classes).
I've 2 types of datasets:
regular (~22000 samples)
dataset of duplicates (~19000 samples)
I've written a logic that labels them all.
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Can I transform multi-class data that has 3 classes to different number of classes?
I've seen cases where the author reduces the number of labels/classes -- e.g. classification of 5-star based reviews to 3 classes (positive, negative, neutral) by changing the label of 4 and 5 stars ...
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Is there a way to choose the most appropriate option from mapping file while predicting the target label?
I have a multi class classification problem where I am predicting a target label using two error text fields.
Input data looks like this:
SNo.
Error Description
Error Trace
Target Label
1
some text
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terminology and advice for NLP on multiclass classification with ordered levels
I work in healthcare and am trying to see if I can use NLP for a classification task on complex sentences. To explain, I have different labels, and each label has multiple levels. I am not sure on the ...
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How to understand a large result of torch.nn.NLLLoss() with correct predicts?
I'm learning the usage of torch.nn.NLLLoss() and torch.nn.LogSoftmax(), and I'm confused about the results of them.
For example:
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How can I get the score of each Prediction for a Multiclass Classification model
I am just curious, and I wanted to know if it is possible to get the score of each prediction in a Multiclass Classification model. If it is possible, how can I implement this to make predictions on ...
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Algorithm suggestions for a clustering problem
I have a collection of images and each of those images has a set of tags attached to them.
There are around 30k images
There are around 1k unique tags
The least tagged image has 3 tags, and the most ...
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Should I drop duplicates or not?
I am dealing with a real-data multiclass classification problem. The task is to classify the kind of fault for some equipments.
Features in input are equipments' alarms (X), the target is the fault ...