Feature Scaling
Explore how to apply feature scaling to text data transformed into numerical TF-IDF features. Learn why scaling is crucial for distance-based algorithms, computational efficiency, and consistent visualization. Understand the process of using Python tools like TfidfVectorizer and MinMaxScaler to normalize text features for better model performance.
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Introduction
Feature scaling is a text preprocessing technique that ensures that different features in a dataset are on a similar scale to improve the performance of machine-learning models. It’s important to note that we don’t apply it directly to text. Instead, we apply it to the result of other text representation techniques, such as BoW or TF-IDF. These techniques convert the text into numerical representations, to which we can later apply feature scaling.
Reasons for feature scaling
Here are some reasons why feature scaling is important in text preprocessing:
Improved performance of distance-based algorithms: Algorithms that rely on distances, like
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