Description: Z-score adjustment is a statistical process used to modify data in order to improve the performance of machine learning models. This method is based on normalizing data, where each value is transformed into a Z-score, indicating how many standard deviations a data point is from the mean of its set. This approach is crucial in data preprocessing, as it allows machine learning algorithms to operate more efficiently and effectively. By standardizing data, biases that may arise from different scales and units of measurement are minimized, facilitating comparison and analysis. Additionally, Z-score adjustment helps improve the convergence of optimization algorithms, allowing models to learn patterns more quickly and accurately. In summary, this process is a fundamental tool in data preparation, ensuring that machine learning models operate in a balanced and optimized environment.
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