What are some machine learning data organization techniques?

1 Data cleaning

Data cleaning is the process of removing or correcting any errors, inconsistencies, outliers, or missing values in the data. It is essential for improving the quality and accuracy of the data, as well as reducing the noise and bias that can affect the machine learning models. To do this, one can replace or impute missing values with appropriate methods like mean, median, mode, or regression. Additionally, duplicates, typos, or invalid entries can be detected and removed. Furthermore, data can be normalized or standardized to have a common scale or range. Finally, outliers or extreme values can be handled by removing them, clipping them, or transforming them.

2 Data transformation

Data transformation is the process of changing the format, structure, or representation of the data to make it more suitable for machine learning algorithms. This process can enhance features, reduce complexity, or increase compatibility. Examples of data transformation techniques include encoding categorical variables into numerical values (e.g. one-hot encoding, label encoding, ordinal encoding), discretizing continuous variables into bins or intervals (e.g. equal-width, equal-frequency, entropy-based binning), scaling or rescaling the data to have a specific range or distribution (e.g. min-max scaling, standard scaling, log scaling), and generating new features from existing ones (e.g. polynomial features, interaction features, feature extraction methods).

4 Data splitting

Data splitting is an essential process in machine learning, which involves dividing data into different subsets for training, validation, or testing. This helps to evaluate the performance and generalization of the models, as well as avoiding overfitting or underfitting. Common data splitting techniques include the holdout method, which splits the data into two subsets; cross-validation method, which splits the data into k subsets; and bootstrap method, which creates multiple subsets by sampling the data with replacement. Examples include 80/20 split, 70/30 split, 60/40 split, k-fold cross-validation, leave-one-out cross-validation, leave-p-out cross-validation, bootstrap aggregating, bootstrap .632 and bootstrap .632+.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?