Questions tagged [multiclass-classification]

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Multiclass ROC Curve using DecisionTreeClassifier

I built a DecisionTreeClassifier with custom parameters to try to understand what happens modifying them and how the final model classifies the instances of the iris dataset. Now My task is to create ...
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What parameters does the model learn in object classification? [closed]

In the case of object classification, what parameters do we learn from training examples? Do we learn mean and standard deviation? Let's say if I had 50 images with ground truth bounding boxes and ...
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1answer
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How is calculated the error with multiple output neurons in neural network?

Machine Learning books generally explains that the error calculated for a given sample $i$ is: $e_i = y_i - \hat{y_i}$ Where $\hat{y}$ is the target output and $y$ is the actual output given by the ...
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how print f1-score with scikit´s accuracy_score or accuracy of confusion_matrix?

I would like to print the f1-score. I got confused about the wording f1-accuracy score and accuracy score. What is the difference of these 2 scikit-learn metrics and how can I print the f1-score out ...
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Separate weights for XGboost multi classification model? [closed]

I have a dataset that has 2 classes - 0 and 1. I have set the weights in the DMatrix to separate weights (as an array) for all the incorrect predictions. However, ...
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Precision-Recall Curve Intuition for Multi-Class Classification Utilizing SoftMax Activation [closed]

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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Confusion with the solution for this decision theory problem [closed]

$\textbf{I am given the following Decision Theory question:}$ Given a loss matrix with elements $L_{kj}$, the expected risk is minimized if, for each $x$, we choose the class that minimizes $\sum_{k} ...
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1answer
17 views

Is there an intuitive interpretation of precision always higher than recall?

I have a multiclass-classifier whose macro-precision is always greater than macro-recall. I suppose it means false negatives outnumber false positives in general. Is there an intuitive interpretation ...
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1answer
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Which is better: multi-output model or separate models for similar tasks?

I am working on two problems: classification of images into high-level classes (e.g. shoe, dress, jacket etc.) classification of the attributes of the same images on a lower level (e.g. shoe style, ...
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1answer
20 views

How are precision and recall better metrics than accuracy for classification in my example?

I'm trying to understand precision and recall with an intuitive example, but my calculation doesn't seem right. For example, there are 8 red balls and 2 blue ones. I'm stupid and just predict all of ...
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1answer
15 views

Catboost multiclassification evaluation metric: Kappa & WKappa

I am working on an unbalanced classification problem and i want to use Kappa as my evaluation metric. Considering the classifier accepts weights (which i have given it), should i still be using ...
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AUC on ROC Curve near 1.0 for Multi-Class CNN but Precision/Recall are not perfect?

I am building a ROC Curve and calculating AUC for multi-class classification on the CIFAR-10 dataset using a CNN. My overall Accuracy is ~ 90% and my precision and recall are as follows: ...
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Destination prediction with Naive Bayes and sparse output matrix

Given a dataset of historical cab rides, I'm trying to predict the final zip code destination of a ride based on the following features: origin zip code (e.g. 10006 Wall Street, Manhattan) pickup ...
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Is it possible to combine SMOTE undersampling and oversampling in a multiclass classification problem when balancing an imbalanced dataset?

I'm using SMOTE to balance my very imbalanced dataset. I get good results by oversampling alone, but I would like to try oversampling and undersampling my dataset. I understand that one can set the ...
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Can we use CNN to effectively learn from a table whose various rows are permuted in the testing dataset, output for each remains same?

I have a set of classes, 37 to be precise. Each class is represented by a feature vector of size [1,10]. A single input sample has the dimensions of ...
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1answer
46 views

Modifying binary classification to Multi-Class Classification (Logistic Regression) [closed]

I am using this code that I found here for logistic regression for binary classification with 2 classes. Git Repo The data that I am testing with is calling for multi-class classification (6 classes). ...
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split flattened tensor to perform class wise loss

I would like to adapt my dice loss function for multi-class averaging. y_true and y_pred are one-hot 3D images (mask of label). For example, if the flattened tensors contain the X classes sequentially,...
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1answer
17 views

AUC ROC Curve multi class Classification

Here is the part of the code for ROC AUC Curve calculation for multiple classes. ...
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2answers
36 views

How to solve this classification problem: multi-class or multi-label?

In a supervised cancer classification task which is given the data containing metrics we want to classify whether the patient has cancer or is at high risk (label 1) or low risk (0). However, there is ...
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45 views

Multi-class Classification with Sigmoid Activation at Output Layer

In multi-class classification (mutually exclusive classes) using Neural Networks (ANN), it is generally advised that we encode our target labels as one-hot, use a softmax layer as the output with the ...
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Misclassifying only 1 of 3 classes in NN with Keras against RF

I'm working on the classification of a small dataset of clinical data. It is composed of around 3500 samples, with 15 features and there are 3 diferent classes (2 of them also have 2 subclasses each ...
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How to practically deal with unbalanced data and realistic class distributions in a Naive Bayes classifier?

I’ve created a multi class multinomial Naive Bayes classifier for text classification. However my data is unbalanced which I’m sure is affecting the accuracy. For example class A has 500 datapoints ...
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79 views

Class weight adjustment in multi output model error

I'm trying to make a multi output model. I have 3 regression models (reg_1, reg_2, reg_3) and one classification model (cl_1) that classifies 5 classes (0,1,2,3,4). The classification data is not ...
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Cox proportional hazards with multiclass dependent variable

Is it possible to perform a multiclass Cox proportional hazards model? I'm interested in finding the probability of self cure on consumer loans, or just study how self-curing clients behave so as to ...
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2answers
36 views

Image multi class classifier CNN

I have a problem, im designing a multiclass classifier to classify medic images, I have to classify in which grade of desease is it, this are 6 grades , each time the joint deforms a little, so, mi ...
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Is there any feature selection method specific for regression analysis?

Is there any feature selection method that works especially well for regressions? I used backwards elimination and forward selection before a lot but I've recently read that even though it's ...
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32 views

Formulate Confusion Matrix from Precision Score

I have the precision scores from 5 classes. Is it possible to create the confusion matrix from that scores. While running the code, I only saved those precision values by mistake. So now I want to ...
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1answer
26 views

How to create a classification model for multi output dataset?

I have a dataset where there are two target variables target-1 and target-2. Both target variables are ordinal and thus I want ...
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1answer
58 views

Comparing multi-class vs. binary classifiers in predicting a single class

I've pretty much read the majority of similar questions, but I haven't yet found the answer to my question. Let's say we have n samples of four different labels/...
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26 views

Plotting scikit-learn confusion matrix returns no values in the last class

I am attempting to create a confusion matrix using Scikit-Learn for a multiclass classification CNN, and it works well except for the fact that it does not provide ...
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1answer
47 views

Why Adaboost SAMME needs f to be estimable?

I am trying to understand the mathematics behind SAMME AdaBoost: At some stage, the paper adds a constraint for f to be estimable: I do not understand why this is ...
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Multi class product classification according to description of product

I have some products, along with their description. I wish to assign USPSC code to each product. This would require classification on 4 levels. All the examples I could find online were that of ...
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1answer
23 views

Mapping output neurons to classes

I have read few articles some say there is no need to have no. of units in output layer = no. of classes why some say they both should be equal. My questions are If no. of neurons = no. of classes. ...
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How to model user choice probability: binary model vs multi class model

Let's say Morpheus has multiple users to offer colored pills(from an infinite set of colored pills), there are in total 3 unique colored pills(red, blue, green) Morpheus can offer. The trick is, ...
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331 views

Logistic regression does cannot converge without poor model performance

I have a multi-class classification logistic regression model. Using a very basic sklearn pipeline I am taking in cleansed text descriptions of an object and classifying said object into a category. <...
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Keras deep learning speaker identification model excels during training and then fails predictions

I am attempting to create a 1:N speaker identification model with Keras using a TensorFlow backend. I used the LibriSpeech corpus for training data, and preprocessed the data by first converting each ...
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37 views

Is the micro averaged precision/recall/f1 score for multiclass classification always the same?

I was under the impression from this post that in the micro averaging case for multi-class classification, the precision and recall are the same. This is because the number of false negatives and ...
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1answer
245 views

Confusion between precision and recall

I have a machine learning model that try to fingerprint the functions in a binary file with a corpus. Final output of upon inputing a binary file is a table with one to one mapping between the binary ...
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1answer
52 views

Increase Accuracy on Keras Multiclass Classifier [closed]

I have a dataset with the shape (430, 17). My output is a single column consisting of possible options "Best", "Medium", "Worst". I split my data into ...
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1answer
23 views

Multiclassification with large number of labels

I am attempting to build a classifier with a large input space of one hot encoded vectors. The output should be a vector of labels, with 10000 possible labels each. For example, the labels could ...
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12 views

proper activation function at output and loss function to optimize for OCR?

I am trying to make a CNN model on IAM handwritten words data(which has images of words handwritten by multiple people and targets are text in the images). So, I can encode words to numbers(A=0, B=1 ...
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2answers
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Why my training and testing set are about 99% but my single prediction does wrong prediction?

I have performed fruits classification using CNN but i am paused at a point where all things are going right confusion matrix accuracy score all are correct it seems there is no overfitting but it ...
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1answer
41 views

Understanding output probabilites of xgboost in multiclass problems

I would like to understand the output probabilities of a xgboost classifier (or any other decision tree ensemble based classifier) in the case of a multiclass problem. For example: We have 5 different ...
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2answers
39 views

Text Classification : Classifying N classes vs rest of the classes

Apologies if this is naive, I am fairly new to the domain. I have a requirement where I am trying to classify 2 types of text data, i.e, I have got 2 classes to classify my data upon. I am able to get ...
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2answers
47 views

Appropriate loss function for multi-hot output vectors

I have some data in which model inputs and outputs (which are the same size) belong to multiple classes concurrently. A single input or output is a vector of zeros somewhere between one and four ...
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1answer
42 views

How to train a machine learning algorithm with multiple labels

I have the following challenge and I very much hope that there is a solution to it. I also suspect that there is a simple approach to it. I just don't see it at the moment. Any help or advice is ...
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1answer
69 views

Time Series Data Multi-Class Classification

This is a very general question, as I'm still very much in the learning phase with machine learning. I have some utility data around problematic meters. Even tho the data is "time series", I believe ...
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72 views

per class IOU & Jaccard Similarity in a Multiclass setting python

For a multiclass classification problem, How do you compute per class IOU ? I am using the formula which is referenced/accepted in the below link ...
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dealing with imbalanced data for multi-class problem

Based on the experiments I run for a number of times, and the reading I did on imbalanced data for a multiclassification problem such as this paper, resampling techniques like ...
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How do people deal with significantly uneven error in NNs

The following example is a common issue with multiclass classification problems: If we try to classify an object - let's say - by color (e.g. white, red, green, blue, black, transparent), a simple ...

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