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 ...
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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 ...
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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, ...
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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 ...
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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 ...
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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 ...
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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 ...
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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)+(...
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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-...
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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:
...
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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 ...
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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 ...
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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 ...
- 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 ...
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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 ...
- 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 ...
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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 ...
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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 ...
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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-...
- 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 ...
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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-...
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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.
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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. ...
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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 ...
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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 ...
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