What is bias in machine learning?
Bias in machine learning is a form of systemic error that occurs when the data used to train or operate a model skews the output or result away from its expected value.
This means that the model cannot accurately represent the population on which it is supposed to be operated, leading to inaccurate predictions, errors, and poorer performance.
Just like bias in statistics, machine learning bias can potentially limit an algorithm's ability to interpret data and make decisions accurately. As a result, it can lead to the algorithm placing more emphasis on certain characteristics or data points and not taking other important factors into account. This skews the model and can lead to decisions that do not reflect reality.
Before delving into bias in machine learning, it's important to understand the techniques used in machine learning model algorithms. There are three primary techniques in machine learning:
All these machine learning methods are susceptible to bias since they rely on data for training and operation.
What is variance in machine learning?
Variance in machine learning measures how much the data points within a dataset differ from each other. When variance is high, the data points have a wide range of values and are spread out.
A high variance model makes predictions too far away from the expected result. The model will likely produce inaccurate predictions even when given data with similar values. The models create vastly different predictions for similar inputs, meaning that the model is over-sensitive to small changes in the input data.
In contrast, when variance is low, it means that the data points are much closer together and have similar values.
Variance errors occur when a machine learning algorithm creates predictions that are too far away from the expected outcome, leading to poor performance or inaccurate results. To avoid variance errors, selecting a model with the right complexity and hyperparameters is important. This will ensure that the predictions generated by the machine learning algorithm are closer to the expected result.
Bias and variance are closely related in machine learning. If a high-bias model is created, it leads to a low-variance model due to the lack of ability to accurately represent the data. On the other hand, if a low-bias model is created, it leads to a high-variance model due to the ability of the algorithm to accurately represent the data.
Common types of bias
Business owners and marketers need to be aware of various types of bias, from algorithmic to human. Understanding the types of biases and how they can influence their decisions is vital to making good, informed decisions.
Biases vary in terms of the level of awareness, but all have the potential to impact decision-making negatively. The following are some of the most common types of bias:
- Algorithm bias
- Confirmation bias
- Data bias
- Human bias
- Anchoring bias
- Recency bias
What causes bias in machine learning
Machine learning models can suffer from bias when trained on datasets containing inequitable or incomplete data. This can lead to biased models against certain populations or groups and inaccurate decisions.
Common causes of bias in machine learning include:
- Unrepresentative training data: If the training dataset does not adequately represent the population, it can lead to biased results.
- Unbalanced datasets: Training datasets that are predominantly composed of one particular class can lead to models that are biased towards that class.
- Unstructured data: If data is not properly labeled or structured, it can lead to biased models towards certain classes.
- Poor data quality: Data with inaccurate or missing values can lead to biased models toward certain classes.
- Prejudiced algorithms: Models that rely on prejudiced algorithms or methods can lead to biased results.
It is crucial to understand the different causes of bias in machine learning and seek ways to avoid them actively. By ensuring that machine learning models are trained on balanced datasets, representative, and of high quality, you can help to build more accurate models. At the same time, it is important to be aware of the potential for biased algorithms and to take the necessary steps to mitigate any risks.