Data preprocessing
Data preprocessing puts data into the right shape and quality for training. There are many data preprocessing strategies including: data cleaning, balancing, replacing, imputing, partitioning, scaling, augmenting, and unbiasing.
Figure 9: Data preprocessing main components
The data preprocessing strategies listed in Figure 9 can be expanded as the following:
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Clean (replace, impute, remove outliers and duplicates): Remove outliers and duplicates, replace inaccurate or irrelevant data, and correct missing data using imputation techniques that will minimize bias as part of data cleaning.
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Partition: To block ML models from overfitting and to evaluate a trained model accurately, randomly split data into train, validate, and test sets. Data leakage can happen when information from hold-out test dataset leaks into the training data. One way to avoid data leakage is to remove duplicates before splitting the data.
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Scale (normalize, standardize): Normalization is a scaling technique in machine learning that is applied during data preparation to change the values of numeric columns in the dataset to use a common scale. This technique assists to verify that each feature of the machine learning model has equal feature importance when they have different ranges. Normalized numeric features will have values in the range of [0,1]. Standardized numeric features will have a mean of 0 and standard deviation of 1. Standardization assists in handling outliers.
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Unbias, balance (detection and mitigation): Detecting and mitigating bias assists to avoid inaccurate model results. Biases are imbalances in the accuracy of predictions across different groups, such as age or income bracket. Biases can come from the data or algorithm used to train your model.
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Augment: Data augmentation increases the amount of data artificially by synthesizing new data from existing data. Data augmentation can assist to regularize and reduce overfitting.
