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How can you handle biased or skewed data in ML models?

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Bias and skew are common challenges in machine learning (ML) models, especially when dealing with real-world data. Bias refers to the systematic error or deviation from the true value, while skew refers to the asymmetry or imbalance in the distribution of the data. Both can affect the accuracy, fairness, and generalizability of the ML models. In this article, you will learn some practical ways to handle biased or skewed data in ML models.

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Sanjay Kumar MBA,MS,PhD

1 Identify the source

The first step is to identify the source and the type of bias or skew in your data. Bias can be introduced by various factors, such as sampling methods, data collection procedures, measurement errors, or human judgments. Skew can be caused by outliers, missing values, or inherent characteristics of the data. You can use descriptive statistics, visualizations, or hypothesis tests to detect and analyze bias or skew in your data. For example, you can use histograms, box plots, or scatter plots to examine the shape and spread of the data.

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Sanjay Kumar MBA,MS,PhD
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The initial step in addressing bias and skew in data is to identify their source and type. Bias may stem from factors like sampling methods, data collection processes, measurement errors, or human judgments, while skew can result from outliers, missing values, or inherent data characteristics. Detecting and analyzing bias or skew can be achieved using descriptive statistics, visualizations, or hypothesis tests. For instance, histograms, box plots, or scatter plots can be employed to assess data shape and distribution.

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2 Transform the data

The second step is to transform the data to reduce or eliminate bias or skew, which can be achieved through various methods. Resampling, for example, involves creating new samples from the original data by adding, removing, or replacing observations. This can help balance the classes, reduce the variance, or improve the representativeness of the data. Scaling is another technique which changes the range or the scale of the data to make it more comparable or compatible. Furthermore, encoding converts the data from one format to another to make it more suitable or meaningful for the ML model. All these methods can help handle categorical data, ordinal data, or textual data in an effective manner.

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3 Adjust the model

The third step is to adjust the model to account for or mitigate bias or skew in the data. Depending on the goal and type of ML model, various methods can be employed. For instance, choosing the right algorithm can be beneficial, as some are more sensitive or robust to bias or skew than others, such as decision trees, random forests, or support vector machines. Additionally, tuning the parameters can optimize the model performance and avoid overfitting or underfitting; parameters such as learning rate, penalty, or class weight can control complexity, regularization, and weighting. Lastly, evaluating metrics can measure or reflect bias or skew in the data or model; for example, F1-score captures precision and recall trade-off while equalized odds assesses disparity between groups.

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Sanjay Kumar MBA,MS,PhD
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The third step in addressing bias and skew in data involves adjusting the machine learning model to account for or mitigate these issues. Several methods can be employed based on the model's goal and type. This includes choosing the right algorithm, as some are more sensitive or robust to bias or skew (e.g., decision trees, random forests, support vector machines). Tuning model parameters, such as learning rate, penalty, or class weight, can optimize performance and prevent overfitting or underfitting. Lastly, evaluating metrics, like F1-score or equalized odds, can help measure or reflect bias or skew in both the data and the model, capturing important trade-offs and disparities.

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4 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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How can you handle biased or skewed data in ML models?

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1 Identify the source

2 Transform the data

3 Adjust the model

4 Here’s what else to consider

Machine Learning

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How can you handle biased or skewed data in ML models?

1

2

3

4

1 Identify the source

2 Transform the data

3 Adjust the model

4 Here’s what else to consider

Machine Learning

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