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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218 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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35 views

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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474 views

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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5k views

AUC-ROC for Multi-Label Classification

Hey guys I'm currently reading about AUC-ROC and I have understood the binary case and I think that I understand the multi-classification case. Now I'm a bit confused on how to generalize it to the ...
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1answer
38 views

Detecting off state in the magnitude of accelerometer data?

I have a univariate time series signal. It's the magnitude of an accelerometer attached to an engine. I need to create an algorithm to detect off state, please see the black lines in the image below....
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1answer
564 views

LSTM Multi-class classification for large number of classes

I want to build a model that classifies 473 classes -product categories-, but I'm facing a problem with loss not decreasing. Data I have almost 3,000 data points for each class -473 classes- (data ...
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1answer
149 views

Recall score for each sample in multilabel classification

Does it make sense to calculate the recall for each sample in a multilabel classification problem? Suppose I have 3 data samples, each having its own true set of labels and predicted set of labels. I ...
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2answers
104 views

is there cross validation for xgb classification for multi labels?

is there cross validation for xgb classification for multi labels? I have been search but can not find any cross validation for xgb classifier is using cross validation for xgb or xgb classifier ...
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423 views

How to get feature importance from RandomForest using scikit-multilearn library?

I am working on multi-label classification problem, binary case. As a target variable there are five columns with 0-1 values. For a model training I use scikit-multilearn library. Below is my code ...
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3k views

SMOTE for multilabel classification

I have a dataset with 77 different labels. Each sample has one or more of these labels. I did some data analysis and found out that the dataset is highly imbalanced - there are a large number of ...
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3answers
882 views

How to apply supervised machine learning when the target label depends on multiple input rows?

The problem is a multi-label classification problem. Now, I know how to train and classify using single row with several attributes. For example, if the dataset looks like the first table from the ...
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1k views

Unable to save the TF-IDF vectorizer

I'm workig on multi-label classification problem. I'm facing issue while saving the TF-IDf verctorizer and as well as model using both pickle and joblib packages. Below is the code: ...
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1answer
370 views

Classification algorithm with multiple output for a set of features

I want to build a classification algorithm that will predict multiple values for a set of features. For instance, lets say I have a customer demographic data like Income, age, sex, city and I want to ...
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2answers
2k views

What is the formula to calculate the precision, recall, f-measure with macro, micro, none for multi-label classification in sklearn metrics?

I am working in the problem of multi-label classification tasks. But I would not able to understand the formula for calculating the precision, recall, and f-measure with macro, micro, and none. ...
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1answer
797 views

Multiclassification Error: NotFittedError: This MultiLabelBinarizer instance is not fitted yet

After picking the model, when I try to use it, I am getting error - "NotFittedError: This MultiLabelBinarizer instance is not fitted yet. Call 'fit' with appropriate arguments before using this ...
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1answer
402 views

Making sense of a accuracy plot for a 5 fold training using random forest

I'm using sklearn.model_selection.learning_curve for 5 fold training of data. The code is as given below. ...
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1answer
261 views

MultinomialNB predict_proba doesnt return labels with the probability

I have a model what looks like this ...
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393 views

Multi label text classification from thousands of labels

I don't have machine learning experties, but I'm working on a project that has text classification requirements in it. The easiest approach I was able to understand was using fasttext.. It worked, but ...
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1answer
161 views

Decision Tree Classifier to classify values based on values of other columns

I have data with multiple labels, for example My X set is fromt second to third column, and I want to classify either first column or the last column, so I made my Y the last column. The goal is so ...
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1answer
82 views

unique predictions for "multi-label multi-output" classification task

Let’s assume that four participants (A, B, C and D) take on five sport-challenges (e.g. swimming, running, ...). Our goal is to predict the placement of each participant for each challenge. Moreover, ...
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3answers
688 views

Multi-label classification based on single-label dataset

I'm looking for a solution to detect different moods/styles expressed by an image. Unfortunately, there is no multi-labeled dataset for this task. The scenario of defining a multi-label ...
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1answer
34 views

Can i build an image classification model where each image has multiple labels?

If I am building a model where I need to predict the vehicle, color of it, and make of it, then can I use all the labels for a single image and build my model around it. Like for a single image of a ...
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1k views

Why does classifier chain ask for at least 2 classes, when I have it

I'm using Classifier Chain with logistic regression and when i try to use fit, i get This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 but I'm ...
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1answer
113 views

Trained CNN individually on multiple images to classify them, how can I now classify a related "set" of these images that correspond to one object?

I have a N object classification examples, each example consisting of a set M individual images of the object at different angles. I've trained M CNNs with the dataset of one particular image angle ...
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2answers
58 views

Classification accuracy based on top 3 most likely classifications

My goal is to recommend jobs to job seekers based on their skill set. Currently I'm using an SVM for this, which is outputting one prediction, e.g. "software engineer at Microsoft". However, consider ...
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19 views

How to best use Large images in training set for deep learning

I would like to ask you about how I should deal with the images I have. They are really large. They have this shape: (3000, 4000, 3). I'm working on a multilabel classification model. And I want to ...
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2answers
126 views

Classification accuracy of a Random Multi-label Classifier

What is the exact accuracy of a random classifier which has n labels (say 1000) where k labels (say 50) are true? Can I say the accuracy of a random classifier has an upper bound of k/n? -Edit- I ...
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1answer
31 views

How to select 500 most pertinents tags among 10000?

Say we have 100,000 documents tagged with 10,000 different tags (Max 5 tag per document). We wish to limit allowed tags to a list of 500 tags. How to select 500 tags in order to cover the largest ...
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2answers
3k views

How to feed data to multi-output Keras model from a single TFRecords file

I know how to feed data to a multi-output Keras model using numpy arrays for the training data. However, I have all my data in a single TFRecords file comprising several feature columns: an image, ...
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0answers
821 views

XGBoost for multi-label image classification

I am trying to use the xgboost classifier for a multi-label and multi-class image classification task. I have a list of images that can have up to 5 different labels in each of them. Before I use the ...
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3answers
716 views

One hot encoding for multiple label(trainy) in .fit() method?

I have a mobile price classification dataset in which I have 20 features and one target variable called price_range. I need to classify mobile prices as low, medium, high, very high. I have applied a ...
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71 views

Multi-label classification with missing labels

I have a neural network that generates a vector that represents the class probabilities. Since it is a multilabel classification problem, I'm supposed to train the network using sigmoid + binary cross-...
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1answer
1k views

Using LSTM for multi label classification

I am trying to use LSTMs to train and predict authors using reviews data and metadata ...
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1answer
21 views

Same probability for all classes

I implemented a fully connected MLP of shape [783 (input), 128 (hidden layer) and 10 (output)] the hidden layer had a sigmoid activation function and the output a sofmax. I tested with the dataset of ...
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1answer
235 views

how can I replicate working of Multi Label Binarizer from sklearn package in R? [closed]

I want to achieve same working of MultiLabelBinarizer from sklearn.preprocessing package in R. I have list of labels for each example (for Predicated and Actual) like below. ...
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1answer
71 views

Risk score from Neural Network classifier (more than 2 categories)

I am trying to use a Neural Network to perform multiclass classification. The classes represent Insurance Risk Level. The most risky level is Level 1, the least risk corresponds to Level 10. The ...
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1answer
249 views

Weighing each label in multi-label classification

If, in addition to predicting labels using a multi-label classifier, I'm interested in predicting the weight of each label, what approach should be taken? To give an example, let's say I'm trying to ...
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1answer
1k views

Split tuples with labeled samples in training, validation and test sets

I was reading through all the internet and i can't find nothing similar what i am looking for, i only saw this topic for pd.DataFrame, np.ndarray and list datasets but i didn't find nothing explaining ...
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40 views

Classify the input set into categories based on pre-defined rule set

I'm trying to solve a problem where there are 2 input files given, Input 1: A set of strings in the format. All the strings start with "A". A-B-C-D A-B-C-3 A-X-Y-Z-4 A-X-5 A-X-P-Q-R A-X-P-Q-S A-M-9-...
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2answers
96 views

Improving classifcation when some are less represented?

I have a multi-class classification problem. It performs quite well but on the least represented classes it doesn't. Indeed, here is the distribution : And here are the classification results (I took ...
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315 views

Big dataset for multi-class classification can't be dasked and split, normal one can't be handled

I have a huge dataframe (550MB), the lending club one available here, and I have to predict the class of the grades. The dask dataframe is : ...
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31 views

What (probabilistic models) can only output decisions when they are certain?

I'm basically looking for approaches, models, algorithms for the following situation (a fault diagnosis problem): I have an input set $\{x_i\}_{i \in \{1..m\}}$ with $n$ binary features of cases (...
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1answer
95 views

Data quality improvement as a part of preprocessing: Imputation

I have a python pandas dataframe representing a superset. The data contains a lot of nulls which I want to overwrite with real values. the superset has: both numerical and categorical data some ...
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1answer
1k views

Using a LinearSVC() for multilabel classification with MultiOutputClassifier() in a pipeline in sci-kit learn

My input data is a (23948,) pandas.Series of strings containing newspaper headlines. My target are 20 labels of the headline (e.g. 'crime', 'politics') each binarily encoded with [0, 1]. The labels ...
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2answers
2k views

Best Way to combine multiple datasets into one model

I want to make a multilabel image classification model that can detect many different labels. For each label, I can get at least 5000 positive examples and 5000 negative examples. However, my question ...
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2answers
22 views

can we have different features for different hyperplanes in SVM?

is it possible if we have different features for different classes of svm? For example one of the hyperplane: $$w_1\cdot \text{age}+ w_2 \cdot \text{ trip duration} +w_3 \cdot \text{ income}$$ and ...
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1answer
992 views

Transform multi-label problem to multi-class problem

What are the downsides of modelling a multi-label problem as a multi-class problem with a single classifier? Let my clarify what I mean. There at least two ways that one multi-label problem can be ...
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55 views

how to reduce the load time of tensorflow text classification model.?

I want to reduce the load time of the model, when testing the model to predict the categories using tensorflow text classification. I am getting the raw data from mongodb collection, which has over 1 ...
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1answer
42 views

Is there an algorithm for categorizing unlabeled samples into K classes? [closed]

I am not sure if this would be considered unsupervised, or semi-supervised learning. I am looking for an algorithm that will take N input arrays of features, and then cluster samples(not features) ...