What are some machine learning data organization techniques?

Machine learning is a branch of computer science that involves creating systems that can learn from data and make predictions or decisions. However, before applying machine learning algorithms, the data needs to be organized in a way that makes sense and facilitates the learning process. In this article, you will learn about some common techniques for data organization in machine learning, such as data cleaning, data transformation, data reduction, and data splitting.

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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.

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Achim Reiz

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Consider the kind of analysis first. Outliers in sensor data can often be discarded. However, if we are sure we have measured correctly, the outliers can be valuable! An example is fraud detection, where we may not know what to look for but are looking for behavior outside the norm. With limited data, more cleaning may make sense. Other preprocessing steps are also advised if there is bias (e.g., because of imbalances). However, if we have a lot of training data and use complex algorithms as neural nets, it may make sense to only use moderate cleaning and let the ML model "learn" what is noise and what is not.

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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).

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Achim Reiz

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The data transformation is a step that defines many of the analysis outcomes. But it is also possibly the most tedious one. If you're working with structured data, great Low-Code or even No-Code tools are available that can help make the right decisions while being interoperable with de-facto machine learning standards (e.g., pycaret).

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3 Data reduction

Data reduction is the process of reducing the size or dimensionality of the data to make it more manageable and efficient for the machine learning algorithms. By utilizing data reduction techniques, computation can be sped up, storage space can be reduced, and the curse of dimensionality can be avoided. Examples of data reduction techniques include sampling the data to select a subset of observations or instances that represent the whole population, such as random sampling, stratified sampling, or cluster sampling; feature selection to choose a subset of features or variables that are relevant and informative for the machine learning task, such as filter methods, wrapper methods, or embedded methods; and feature extraction to create a new set of features or variables that capture the essence or structure of the original data, such as principal component analysis, linear discriminant analysis, or autoencoders.

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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+.

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Achim Reiz

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1. Data splitting comes at the very beginning, even before augmentation, standardization, and dimension reduction. 2. Each of the datasets should be a fairly good representation of the phenomenon you're researching. 3. The popular ML libraries all have tools for data pipelining. You don't need to split the data yourself. Otherwise, one quickly makes mistakes in mixing test and training data when trying out various configurations (also known as data leakage).

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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?

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Achim Reiz

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I think ML algorithms and tools often look overwhelming. Yes, there are use cases for when to use a deep neural net. But a lot of analysis, in my experience, is numerical. Great No-Code/Low-Code tools enable quick data exploration and an easy starting point for playing around. Examples are pycaret, which allows exporting models to scikit-learn (so the tool is not a dead end). Knime is also free and has a graphical user interface. And, of course, there are commercial offerings from Azure, AWS, and Google. I would not go down the rabbit hole setting up and configuring all manually from the start, but only when needed.

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What are some machine learning data organization techniques?

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5

1 Data cleaning

2 Data transformation

3 Data reduction

4 Data splitting

5 Here’s what else to consider

Computer Science

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What are some machine learning data organization techniques?

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1 Data cleaning

2 Data transformation

3 Data reduction

4 Data splitting

5 Here’s what else to consider

Computer Science

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