# Introduction Classification is one of the most common tasks in machine learning. The goal of a classification task is to determine the label(s) of a given example. For example, in the LabEx course classification task, given the title of a course, the task is to determine the type of the course. There are different types of classification tasks, such as: - Single-label classification: Each example has only one label. For example, [**Your First Python Lab**](https://labex.io/skilltrees/python/labs/92673) belongs to the label "Beginner" among the labels "Beginner", "Intermediate" and "Advanced". - Multi-label classification: Each example can have multiple labels. For example, [**Random Number Generationw with NumPy**](https://labex.io/skilltrees/numpy/labs/34635) belongs to both the "random", "sqrt" and "sum" skills. - Multi-class classification: Each example can belong to multiple categories, and each category has its own set of labels. For example, [**Linear Regression**](https://labex.io/skilltrees/sklearn/labs/20799) belongs to the "Pro" category in terms of course type, and belongs to the "linear_model", "sklearn", "numpy", "matplotlib" and "pandas" categories in terms of course direction. In this challenge, we will be focusing on performing one-hot encoding for label data. Given a list of labels and the labels of the examples, the task is to encode the labels using one-hot encoding. One-hot encoding involves creating a binary representation of the labels, where each category is represented by a unique position in the encoded sequence. The goal is to complete the `label_process` function in the provided `label_process.py` file to perform the one-hot encoding.
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