72 votes

What is the difference between Multiclass and Multilabel Problem

Multiclass classification means a classification task with more than two classes; e.g., classify a set of images of fruits which may be oranges, apples, or pears. Multiclass classification makes the ...
user avatar
  • 1,081
40 votes

What are the measure for accuracy of multilabel data?

(1) gives a nice overview: The Wikipedia page n multi-label classification contains a section on the evaluation metrics as well. I would add a warning that in the multilabel setting, accuracy is ...
user avatar
30 votes

Multilabel classification metrics on scikit

The subset accuracy is indeed a harsh metric. To get a sense of how good or bad 0.29 is, some idea: look at how many labels you have an average for each sample look at the inter-annotator agreement, ...
user avatar
28 votes

What is the difference between Multiclass and Multilabel Problem

To complement the other answers, here are some figures. One row = the expected output for one sample. Multiclass One column = one class (one-hot encoding) Multilabel One column = one class You ...
user avatar
15 votes

Would a Random Forest with multiple outputs be possible/practical?

Multiple output decision trees (and hence, random forests) have been developed and published. Pierre Guertz distributes a package for this (download). See also Segal & Xiao, Multivariate random ...
user avatar
14 votes
Accepted

Multi-label or multi-class...or both?

Definitions. In a classification task, your goal is to learn a mapping $h: X\rightarrow Y$ (with your favourite ML algorithm, e.g CNNs). We make two common distinctions: Binary vs multiclass: In ...
user avatar
  • 2,222
11 votes
Accepted

What is the difference between a multi-label and a multi-class classification?

Based on the sentence you quoted, each item belongs to one class but can have several labels. Imagine you have animals like a fox, a chicken and a common European viper. A multi-class classification ...
user avatar
  • 8,343
10 votes
Accepted

Why does keras binary_crossentropy loss function return wrong values?

A mistake in your code: $$-\frac{1}{N}\sum_{i=1}^N [\color{red}{\hat{y}_i} \log(\hat{y}_i)+(1-y_i) \log(1-\hat{y}_i)]$$ It should be $$-\frac{1}{N}\sum_{i=1}^N [\color{blue}{y_i} \log(\hat{y}_i)+(...
user avatar
8 votes

Neural network for multi label classification with large number of classes outputs only zero

Tensorflow has a loss function weighted_cross_entropy_with_logits, which can be used to give more weight to the 1's. So it should be applicable to a sparse multi-...
user avatar
  • 263
7 votes
Accepted

scikit multi label classification

The Multi-label algorithm accepts a binary mask over multiple labels. So, for example, you could do something like this: ...
user avatar
  • 206
7 votes
Accepted

Theoretical justification for training a multi-class classification model to be used for multi-label classification

A softmax output layer does not seem to make sense. The total probability of all classes would then be coerced to sum to 1. This does not make sense in a multi-label setting. Using a sigmoid instead ...
user avatar
  • 22.2k
5 votes

Multilabel Classification with scikit-learn and Probabilities instead of Simple Labels

Let me try to answer this, I will edit the answer as I have more information. In general scikit-learn does not provide classifiers that handle the multi-label classification problem very well. That's ...
user avatar
  • 259
4 votes

How to use scikit-learn's cross validation functions on multi-label classifiers

You might want to check: On the stratification of multi-label data . Here the authors first tell the simple idea of sampling from unique labelsets and then introduce a new approach iterative ...
user avatar
  • 431
4 votes
Accepted

One-class SVM vs. OneVsRestClassifier for multi-label text classification task

Yes, I've done this very same task. The OneVsRestClassifier in scikit-learn can be used for multi-class or multi-label classification. (when used in multi-label ...
user avatar
  • 990
3 votes
Accepted

Multi-label classification problem: choosing the right threshold value for y = 1

It is actually extremely important what perfomance measure you select and it should be relevant to the domain you are working on. Dembczynski et. al have a great paper that shows that selecting a ...
user avatar
  • 259
3 votes
Accepted

Classifier accuracy decreases as n of n-gram models increases. Is this expected?

The problem with spurious variables in Random Forest is that each tree selects only a random subset of features, and if their number gets high, so if most of them are bogus (which is most likely the ...
user avatar
3 votes

Why is softmax considered counter-intuitive for multi-label classification?

It is actually the opposite of what you said "The sigmoid binary loss would encourage the true label logits to be much higher than 0, and the other logits to be much smaller than 0. I think ...
user avatar
2 votes

What is the difference between Multiclass and Multilabel Problem

And one more difference lies in that the multi-label problem requires the model to learn the correlation between the different classes, but in multiclass problems different classes are independent of ...
user avatar
  • 5,141
2 votes

Would a Random Forest with multiple outputs be possible/practical?

As stated here: All classifiers in scikit-learn do multiclass classification out-of-the-box. And that includes Random Forest. Also the page: http://scikit-learn.org/stable/modules/tree.html#tree-...
user avatar
  • 559
2 votes
Accepted

Bad results using Bayes Multinomial Navie in multi-label classification texts

One plausible explanation is the high correlation of your features. I am by no means an expert on NLP, but you could check the following hypothesis: tf-idf is proportional to the frequency a word ...
user avatar
  • 13.1k
2 votes

Multi-label classification

In the next version of mlr that will come out on monday, there will be many multilabel algorithms available in R. For learning how to use it, you can use the tutorial: http://mlr-org.github.io/mlr-...
user avatar
  • 1,019
2 votes

Multi-label classification

Predict each label independently. Because objects may have more than one label. Thus, this isn't really a multi-class problem, because the classes aren't disjoint.
user avatar
2 votes

How to plot visualization for multi-label k-Nearest Neighbor?

This is the best visualization I can attempt to use to describe multi-label KNN. Let me know if you disagree. In the plot below, individuals are one or more of the labels: {blue, orange, green}. As ...
user avatar
2 votes

Rationale for Multi-Label vs. Single-Label learning?

Note that there is a subtle but important difference between multilabel problems, in which each instance may belong to several classes, and multiclass problems, in which each instance belongs to one ...
user avatar
  • 17.5k
2 votes

Algorithm: multi label classification

I'm highlighting feature correlation as possible solution to your problem - which is different to multilabel classification, but might be able to give you a simple answer to your question. I guess ...
user avatar
  • 3,701
2 votes

Keras multilabel text classification

A shameless plug over here. Feel free to check Magpie, a framework for multi-label text classification that builds on word2vec and neural network technologies. It ...
user avatar
  • 574
2 votes

how to make new class from the test data in machine learning

There are several ways to do this, I'll just list two: Train a one versus all classifier $\psi_k$ for each class $k$ in your training set. Predict the test set with each one of those. Test instances ...
user avatar
  • 17.5k
2 votes
Accepted

Is it a good idea to normalize the data consecutively in two different methods?

The part about normalizing across rows pops out at me. It's usual to normalize a feature (column) so that, having done this for each feature, the features will be on more comparable scales. ...
user avatar
  • 20.1k
2 votes

Sequence to Label Model

It would be helpful if you could provide some more information about the structure and characteristics of your data, without this any advice is kinda like shooting in the dark. But until then here are ...
user avatar
  • 191
2 votes

How to intuitively understand difference between algorithmic adaptation vs problem transformation in multi-label classification?

Let's see. Multi-label classification is the problem of assigning multiple labels (categories) to each input sample. The classical example is blog posts and tags. Say you want to train an algorithm ...
user avatar
  • 990

Only top scored, non community-wiki answers of a minimum length are eligible