Data Reduction
Data Reduction is the process of reducing the amount of data without losing its integrity and usefulness. It involves techniques such as data compression, feature selection, and feature extraction. Data compression is the process of reducing the size of the data by encoding it in a more efficient way. Feature selection is the process of selecting a subset of relevant features from the original dataset. Feature extraction is the process of transforming the original features into a new set of features that are more compact and informative. Data reduction is important in data science because it helps to improve the efficiency of data storage, processing, and analysis. It also helps to reduce the risk of overfitting and improve the accuracy of machine learning models.
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