Equal Frequency Binning
Equal Frequency Binning is a data preprocessing technique used in data science and machine learning to transform continuous variables into categorical variables. It involves dividing a continuous variable into a set of intervals or bins, where each bin contains approximately the same number of observations. This technique is useful for reducing the impact of outliers and for handling skewed data distributions. Equal Frequency Binning can be used to discretize numerical data, which can then be used as input for machine learning algorithms that require categorical data. The number of bins can be determined based on domain knowledge or through statistical methods such as the Freedman-Diaconis rule or Sturges' rule.
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