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Results tagged with multiclass-classification
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Multi-class classification is when you have a classification problem with multiple classes, specifically 3 or more classes. Many classifications are binary by design, therefore the additional nomenclature of multi-class classification was defined to describe algorithms capable of classifying datasets with more than 2 classes.
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What does it mean that classes are mutually exlcusive but soft-labels are accepeted?
What does it mean that classes are mutually exclusive but soft-labels are accepted?
As it can be seen from here, tf.nn.softmax produces just the result of applying the softmax function to an inpu …
- 14.2k
2
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Which Classification Metrics Are Appropriate For Each Class Distribution Scenario?
First of all don't change the distribution of your data. Your classifier won't perform good at test time if the real data is not balanced.
If you have approximately equal amount of data for each clas …
- 14.2k
1
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Multi-class classification with extremely small dataset
Yes, you can employ different methods. You can employ deep learning models, but you should not train them from scratch. You should employ transfer learning. Due to the fact that your dataset is small, …
- 14.2k
0
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Multiple output classes in keras
You have label of classes which are not mutually exclusive which means as the label of each sample, your data won't be in one-hot-encoding format. Each output vector may have multiple ones. In such oc …
- 14.2k
1
vote
Accepted
What loss function should I use if I have been working on a classification problem which inv...
The labels of data are not mutually exclusive so you can't say this is a one vs. all problem, because more than one entry may be one in the output vector. Moreover, if in the seen there should be an a …
- 14.2k
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How to cross-validate a deep learning model for highly imbalanced datasets?
You have imbalanced data set, so you should use F1 score. Also you can use weight for rare classes, so that your cost function will be formed in a way that it cares about rare classes so much and trie …
- 14.2k
2
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Evaluation methods for multi-class classification
You have to use F1 score. A simple solution for that is to use confusion matrix. The way you can find F1 score for each class is simple. your true labels for each class can be considered as true predi …
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0
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Which method should be considered to evaluate the imbalanced multi-class classification?
For imbalanced datasets you can employ F1 score. It considers both rare and common classes. You can take a look at this article if you are not familiar with that.
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0
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Why do people use CrossEntropyLoss and not just a softmax probability as the loss?
They are tools for different purposes. Softmax is used in cases that you have labels which are mutually exclusive, they should be contradictory, and exhaustive, one of the labels should always be one …
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2
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Accepted
Should estimated probabilities from multi class classification sum to 1
Summing up to one or not both can have their special meaning that I'll try to explain them. If you have classes that are not mutually exclusive, say you have dog and cat classes and they both can exis …
- 14.2k
12
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How does Sigmoid activation work in multi-class classification problems
If your task is a kind of classification that the labels are mutually exclusive, each input just has one label, you have to use Softmax. If the inputs of your classification task have multiple labels …
- 14.2k
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How to find the most important attribute for each class
Let's say that in this way. The easiest way to ascertain the relation among different features and the outputs is to use covariance matrix. You can even visualise the data for each class. Take a look …
- 14.2k