Percentiles in machine learning refer to statistical measures that divide a dataset into equal or unequal segments. They are used to understand the distribution and characteristics of numerical data. A percentile represents a specific value below which a given percentage of the data falls. For example, the 75th percentile indicates that 75% of the data points are below that value.
In machine learning, percentiles are valuable for various purposes, such as:
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Outlier Detection: Percentiles can help identify outliers in a dataset by comparing data points to certain percentile thresholds.
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Data Analysis: Percentiles provide insights into the distribution and spread of data, enabling researchers to understand patterns and trends more effectively.
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Feature Engineering: Percentiles can be used to transform numerical features into categorical features by mapping data points to different percentile ranges.
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Data Preprocessing: Percentiles are helpful in handling skewed distributions by normalizing or transforming data based on percentile ranks.
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Decision Making: Percentiles assist in making data-driven decisions by providing quantifiable measures for comparison and evaluation.
Overall, percentiles play a crucial role in machine learning, aiding in data exploration, preprocessing, and decision-making processes.
