Questions tagged [multilabel-classification]

Multilabel classification assigns to each sample a set of target labels. This can be thought as predicting properties of a data-point that are not mutually exclusive, such as topics that are relevant for a document. A text might be about any of religion, politics, finance or education at the same time or none of these.

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Softmax regression cost function code [closed]

I really do not understand what does this code do M = sparse.coo_matrix(([1]*n, (Y, range(n))), shape=(k,n)).toarray() The code is related to calculating the ...
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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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2-label dataset for 3-label classifier?

I have a dataset containing headlines and sentiment related to those headlines. The headlines have been filtered out from another bigger dataset using the following criteria: keep the ones that have a ...
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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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How to find the feature regions where each label is the most expected when using decision trees?

Given a decision tree for classification for example this one: What is the way to find the feature domain (petal and sepal width and length) where a sample would most likely occur in the feature ...
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Validation error is always zero in a multi class classification problem. What could be the reasons?

I have a 3 class(1/0/unclassified) classification problem where my training data is created using a bunch of rules. Problem: Classify whether a person owns a vehicle or travels by public transport. ...
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ML - Labelling - number of possibilities [closed]

I've started learning ML and stuck with the number of possibilities in labelling. I have a sample which comprises 4 attributes (binary) (from the book Apprentissage artificiel, Antoine Cornéujols) ...
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Which model for a content suggestion in which only some of the items are currently available

I am trying to understand the type of model that would be used in a content suggestion scenario where not all of the choices are available at a given time. For example, when an online movie ...
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ValueError: bad input shape

I have multilabel problem. I was using onevsrestclassifier and now i want to use onevsoneclassifier. ...
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How many outputs for CNN when dealing with a multi-label classification problem with OneHot Encoded labels?

My labels are of type tensor [1 0 0 1] denoting a 4 label multiclass problem for any given 16x16 image. I'm using BCEWithLogitsLoss from ...
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MultiLabelBinarizer() with inverse_transform()

I have multilabel labels. Elements in a label mean voting. Here is how labels look: ...
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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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Multilabel Classification - Overfitting?

My task is the following: To input drug combinations and output renal failure-related symptoms from the drug combinations. Both the drug combinations and renal-failure related symptoms are represented ...
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How can I label (predict) an unseen set of data based on an existing model?

I'm working on a learning multi-label classification project, for which I've taken 16K lines of text and kind of manually classified them achieving around 94% of accuracy/recall (out of three models). ...
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Improving misclassification for one class in a multi-class classification task

Here I am trying to use 3 convolution layer neural network to classify a set of images (train data: (3249) , validation data: (487), test data: (326)) I have one class which is misclassified and I ...
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Is it better to have one model with more categories or less with two for multi-label classification?

For classifying text into three classes question, complain and complements where each sample can have multi-labels (question and complains, question and complements): is it better to have one model ...
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Positive/negative training sample imbalance in multi-label image classifiers

I'm trying to train VGG-16 on the Pascal VOC 2012 dataset, which has images with 20 labels (and a given image can have multiple classes present). The examples are highly imbalanced, so I've "...
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Is there a deep learning method for 3D labels?

As the question says, I want to feed labels into a neural net that are three dimensional. Let's say that I have 3 possible labels and each one of my data points corresponds to a percentage of those ...
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Why Overfitting sometimes appears when compile model multiple time, is it normal?

At the time I got small datasets of brainwaves (EEG) (105 samples) for 3-class classification problem. I split my data into 3 part: Train data = 90 (data) Validation data = 10 (data) Test data = 5 (...
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Evaluating a Multi-Label Classification model

I currently have a multi-label classification problem, for which I am using keras to build a neural network as follows: ...
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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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Can reducing the number of classes in multi-label classification increase performance?

This is more of an open question with people which have experience in this. I'm working on a multi-class multi-label classification for chest x-rays. I would like to know how much can reducing the ...
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Reframing multilabel classification with imbalance in “both” directions

Consider the multilabel problem when asking "does the sample belong to this class" with, for example, a movie label dataset where almost every movie is labelled "drama" because of ...
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how to classify text based on more than one column

I passages of text to classify by topic. I am using scikit learn, e.g. linear svc, but open to other options. Currently, use only the text of each passage (column labeled ...
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How to divide a dataset for training and testing when the features and targets are in two different files?

I am trying to divide a dataset into training dataset and testing dataset for multi-label classification. The datset I am working on is this one. It is divided into a file which contains the features ...
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1answer
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Classification model to classify large number of classifiers?

Hello I am very new into the field of machine learning/deep learning , and I am finding it hard to select the right model for my research. What I am trying to build is a model to classify which ...
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multi-label prediction with pySpark

I am new to Spark I am using pyspark to predict a multi label results. I have converted multi labels to binary So my labels will look like this ...
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SKLearn NearestCentroidClassifier score with predict_proba

I'm using the NearestCentroidClassifier combined with TF-IDF for classification of documents. The are linked to a growing number of document groups. I've set sklearns TfIdfVectorizer and the ...
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Using pos_weight with BCEWithLogitsLoss to improve recall in a multi-label problem

I have a multi-label classification problem, and so I’ve been using the Pytorch's BCEWithLogitsLoss. I’d like to optimize my model for a higher F2 score, and so want to bias it to have greater recall (...
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1answer
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Random Forrest Sklearn gives different accuracy for different target label encoding with same input features

I'm using sklearn Random Forrest to train my model. With the same input features for the model I tried passing the target labels first with label_binarize to create one hot encodings of my target ...
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Doing q individual train/test splits for q possible labels in a multilabel problem?

Let's say that I have a multilabel problem, where each sample can be of class A, B, C, or any combination of these. Because of high imbalance, I've found that if I tackle the problem as 3 separate, ...
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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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How to explain a stable NDCG@K in extreme multilabel recommender model

I am working in a multilabel recommender project and I try to evaluate it as a ranking problem. I calculate recall@k and precision@k which both looks quite well. Recall increases and Precision ...
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TensorFlow Time Series Tutorial Enhancement Gone Wrong

I’ve been following this time series tutorial for Tensorflow… https://www.tensorflow.org/tutorials/structured_data/time_series And it was going good, and seemed to work ok. I substituted with my ...
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What is the better way to predict classes for the models developed using the functional API in Keras

We can predict the class for new data instances using the Sequential classification model in Keras using the predict_classes() function. What is the way to predict the class for models that developed ...
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Machine Learning - Multilable Text Classification

I am trying to solve a multilable text classification problem and used tf-idf for feature engineering and calibrated+linearSVC into the model. Results are great, however, I am trying to figure out a) ...
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1answer
632 views

ValueError: The number of classes has to be greater than one; got 1 class in MultiLabel Classification problem

I am working in Python in a Multilabel Classification problem. I have a dataset with texts and around 20k unique labels. I transformed the text to word embeddings and now I use that in a ...
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Time series multiclassification on process measured multiple times

I have been measuring the power usage of a 3D printer for a while. To create a dataset I've measured the power usage of the printer during different printing processes a few times. The data is ...
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1answer
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How can I annotate labels to images automatically?

I have 80 classes, for each class, there are 100 images. I want to label all images for object detection. For this task I have downloaded the LabelIMG tool but it's taking a long time to do it. How ...
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27 views

Multiple Outputs LSTM

I am trying to create a neural network capable of classifying the type of music that a user normally listens to.The idea is that the neural network will receive a 2D input matrix. These matrix ...
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2answers
137 views

Text classification into thousands of classes

Could somebody point me to a paper or code that is about classifying texts into potentially thousands of categories (topics)? I do have data based on Wikipedia and the number of categories is really ...
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Monotonicity of Jaccard and Dice in multilabel datasets

I understand that Jaccard and Dice follow a monotonic relation on binary datasets because the two are related as $J = {S \over {(2 - S)}}$, and I guess this would be the case when micro-average is ...
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What is the better model architecture and setting when using merge layers?

I am building a deep learning model with dense, dropout, and merge layers. The inputs will be N sentences' feature encoded by BERT (768 dim) and then each will go into the same dense layer as the ...
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How to implement a LSTM for multilabel classification problem?

I would like to develop an LSTM because I have a variable input matrix. I am zero-padding to a specific length of 800. However, I am not sure of how to classify a certain situation when each input ...
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113 views

How to interpret Keras predict output?

I am new in Keras and would want to apply a neural network on this dataset: https://www.drivendata.org/competitions/57/nepal-earthquake/ I have proprocessed the dataset transforming categorical ...
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190 views

How to trust the labels generated using ML models?

I have a dataset of patient records. But I do not know whether he is +ve for a cancer or not. So, I do not have the labels in my dataset. Now I can run a machine learning models like clustering to ...
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Classification Model showing different accuracy for SAME data?

This is my first post here, so kindly pardon any commonplace errors. So, i have been training an XGBoost multi-class model on Google Colab. I am using a balanced dataset, with 31000 rows, where each ...
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Multi-Label Loss function and model training

I'm working on Multi-Label problem i.e output can predict 1 or more label as an output and hence training data also have multiple labels. Somehow I'm not able to map such ML model training. Please ...
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How Hyper-linked library vs traditional library differs from each other as ML problem?

Traditional library can be understood as a system, that archives the collective information from the mediums produced by our society, by indexing them to shelves. It is assumed that libraries have ...

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