<p>There are several loss functions that you can use for binary classification. For example, you could use the <a href="https://en.wikipedia.org/wiki/Cross_entropy" rel="nofollow noreferrer"><strong>binary cross-entropy</strong></a> or the <a href="https://en.wikipedia.org/wiki/Hinge_loss" rel="nofollow noreferrer"><strong>hinge</strong></a> loss functions.</p>
<p>See, for example, the tutorials <a href="https://machinelearningmastery.com/binary-classification-tutorial-with-the-keras-deep-learning-library/" rel="nofollow noreferrer">Binary Classification Tutorial with the Keras Deep Learning Library</a> (2016) and <a href="https://machinelearningmastery.com/how-to-choose-loss-functions-when-training-deep-learning-neural-networks/" rel="nofollow noreferrer">How to Choose Loss Functions When Training Deep Learning Neural Networks</a> (2019) by Jason Brownlee. Have also a look at Keras <a href="https://keras.io/losses/" rel="nofollow noreferrer">documentation of its available loss functions</a>.</p>

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2019-07-08T12:17:42.840Z
2021-12-10T21:24:25.420Z
2021-12-10T21:24:25.420Z
nbro
<p>I plan to create a neural network using Python, Keras, and TensorFlow. All the tutorials I have seen so far are concerned with image recognition. However, the goal of my program would be to take in 10+ inputs and calculate a binary output (true/false) instead.</p>
<p>Which loss function should I use for my task?</p>

<neural-networks><machine-learning><classification><objective-functions><binary-classification>
Which loss function should I use for binary classification?
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# Which loss function should I use for binary classification?

(Asked by: username_0 on 2019-07-08)

I plan to create a neural network using Python, Keras, and TensorFlow. All the tutorials I have seen so far are concerned with image recognition. However, the goal of my program would be to take in 10+ inputs and calculate a binary output (true/false) instead.

Which loss function should I use for my task?

**Tags:** neural-networks, machine-learning, classification, objective-functions, binary-classification


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## 1 answers

### Answer by username_1 on 2019-07-08. Score: 1 | Accepted answer

There are several loss functions that you can use for binary classification. For example, you could use the [**binary cross-entropy**](https://en.wikipedia.org/wiki/Cross_entropy) or the [**hinge**](https://en.wikipedia.org/wiki/Hinge_loss) loss functions.

See, for example, the tutorials [Binary Classification Tutorial with the Keras Deep Learning Library](https://machinelearningmastery.com/binary-classification-tutorial-with-the-keras-deep-learning-library/) (2016) and [How to Choose Loss Functions When Training Deep Learning Neural Networks](https://machinelearningmastery.com/how-to-choose-loss-functions-when-training-deep-learning-neural-networks/) (2019) by Jason Brownlee. Have also a look at Keras [documentation of its available loss functions](https://keras.io/losses/).


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