## Deep learning Implementation: Multilabel Classifier

# No Bullshit: Multi-label Text classification Using Keras

## Discusses Implementation details with code

2 min readFeb 8, 2022

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The goal of this blog is to dig right into the weeds of implementing a multilabel text classifier using Keras. If you have clicked on this story, I assume you are familiar with the problem formulation and how it differs from a traditional multiclass classifier. So let’s cut right to the chase.

## Implementation notes:

- For a multilabel text classifier, for each training example, we have multiple labels. As a result, LabelBinarizer should be replaced by MultiLabelBinarizer.
- ‘‘Sigmoid’’ activation should be used in the final layer instead of “Softmax” activation.
- Each class in a multilabel setup is assumed to be a Bernoulli random variable and hence we shall use
`binary_cross_entropy`

loss function instead of a`categorical_cross_entropy`

loss. - Finally, the choice of evaluation metric is pivotal while training a multilabel classifier. Traditional evaluation metrics like accuracy do NOT make sense in the multilabel universe. We shall leverage a custom evaluation metric called
**α- Evaluation metric**introduced by Boutell et. al. in Learning multi-label scene classification.

That's all you need to know to train a robust neural network for solving the multilabel classification problem.

## Implementation:

# Final Thoughts:

- This blog post aims at laying out the deal-breaking differentiators between a multiclass classifier and a multilabel classifier.
- If you are looking for a more comprehensive discussion on various aspects of a Multilabel classifier, I would encourage you to check out the blogs mentioned below.

- As always, feel free to reach out to me via LinkedIn if you have any questions or feedback.