Multiclass classification is a machine learning problem where the goal is to predict one of multiple classes from a set of data. For example, a multiclass classification model could be used to predict the type of flower in an image, or the sentiment of a text review. Keras is a high-level Python library for building and training deep learning models. It provides a variety of tools and features for configuring and training multiclass classification models.

To configure a multiclass classification model with Keras, you first need to define a training dataset containing the features and the target values. The target values should be one-hot encoded, which means that each class should be represented by a vector of zeros with a one in the position corresponding to the class.

Once you have defined your training dataset, you can use the Keras API to create a multiclass classification model. Keras provides a variety of pre-built model architectures that you can use for multiclass classification, such as the Sequential model. You can also define your own custom model architectures.

Once you have created your model, you need to compile it. This involves specifying the loss function, optimizer, and metrics that you want to use. For multiclass classification, the most common loss function is categorical cross-entropy.

Finally, you need to train your model. This involves feeding your training data to the model and allowing it to learn the relationships between the features and the target values. Once your model is trained, you can use it to make predictions on new data.