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Data preprocessing can refer to manipulation, filtration or augmentation of data before it is analyzed,[1] and is often an important step in the data mining process. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, amongst other issues.
The preprocessing pipeline used can often have large effects on the conclusions drawn from the downstream analysis. Thus, representation and quality of data is necessary before running any analysis.[2]
Often, data preprocessing is the most important phase of a machine learning project, especially in computational biology.[3] If there is a high proportion of irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase may be more difficult. Data preparation and filtering steps can take a considerable amount of processing time. Examples of methods used in data preprocessing include cleaning, instance selection, normalization, one-hot encoding, data transformation, feature extraction and feature selection.
^"Guide To Data Cleaning: Definition, Benefits, Components, And How To Clean Your Data". Tableau. Retrieved 2021-10-17.
^Pyle, D., 1999. Data Preparation for Data Mining. Morgan Kaufmann Publishers, Los Altos, California.
^Chicco D (December 2017). "Ten quick tips for machine learning in computational biology". BioData Mining. 10 (35): 35. doi:10.1186/s13040-017-0155-3. PMC 5721660. PMID 29234465.
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