Creating features
Features are derived from processed data typically used as input to models. Creating features in a distributed computing environment is fundamental to developing scalable machine learning models. Utilizing distributed computing frameworks, such as PySpark, allows for the efficient processing and engineering of features from vast datasets, ensuring the data is adequately prepared and optimized for model training. This process not only streamlines the preparation of large-scale data but also enhances the quality of features fed into machine learning algorithms, crucial for building robust and accurate models. Let’s look at creating features from the formatted text that we produced last chapter:
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