Machine learning models endeavor
to achieve error free predictions by learning from data [1]. Variance in
machine learning is the measure of accuracy of a model's predictions to changes
in the training data [2]. It is the amount by which the performance of a
predictive model changes when it is trained on different subsets of the
training data. Variance errors are either low (underfitting) or high
(overfitting) variance errors.[3]
Low variance (underfitting) occurs when your model is too simple to pick up variations and patterns in your data. Therefore, the machine doesn’t gain an understanding of the right characteristics and relationships from the training data, and thus performs poorly with subsequent data sets. For example, it might be trained on a red apple and mistake a red cherry for an apple. While High variance (overfitting) occurs when a model is too complicated, with too much detail and random fluctuations or noise in the training data set. The machine mistakenly sees this noise as true patterns, and thus is not able to generalize and see real patterns in subsequent data sets. For example, it might be trained on many details of a specific type of apple and thus cannot find apples if they don’t have all these specific details.[4]
variance originates from the
concept in statistics, representing the degree of spread or variability within
a dataset, and in the context of machine learning, it specifically refers to
how sensitive a model's predictions are to fluctuations in the training data,
often arising from overly complex models that overfit to the training set,
leading to poor generalization on new data
In the real world, the term
variance is regularly used in the domain of artificial intelligence to assess
the spread of data points in a dataset. It is an important concept in
statistics and machine learning, as it assists to measure how much individual
data points differ from the average value. Various industries such as finance,
healthcare, and e-commerce often utilize variance in their AI systems to
analyze trends, make predictions, and detect anomalies [5].
In the field of predictive
analytics, variance is used to examine the precision of machine learning
models. By assessing the variance of model predictions, data scientists can find
out how well the model generalizes to unseen data. In anomaly detection
systems, variance is used to discover outliers or unusual patterns in data that
may indicate fraudulent activity. Additionally, in portfolio management,
variance is employed to measure the risk associated with investments and
optimize asset allocation strategies. Overall, the concept of variance plays a
vital role in enhancing the performance and efficiency of AI systems across
various industries. By understanding the variability of data within a dataset,
companies can make informed decisions and optimize their operations [5].
- Bias: Represents the error caused by a model being too simple and missing important patterns in the data, essentially underfitting the training data.
- Overfitting: When a model learns the training data too closely, including noise, leading to poor performance on new data due to high variance.
- Underfitting: When a model is too simple and fails to capture the underlying patterns in the data, resulting in high bias and low variance.
A real-life case study of
variance in ai been practiced can be seen in the situation where Netflix
employs variance-based techniques in their recommendation algorithm to balance
exploration and exploitation.
By exploitation, I mean using
what the system already knows works well. This means leveraging existing
knowledge to make reliable decisions that have proven successful in the past.
For Netflix, this would mean recommending more shows very similar to what a
user has already watched and rated highly. By exploration, I mean taking risks
by trying new approaches or options that might lead to better outcomes. This
involves venturing into unknown territory to discover potentially valuable
alternatives. For Netflix, this means recommending shows a user hasn't watched
before and might be outside their typical viewing patterns.
This approach helps Netflix more
effectively introduce new content to the right subset of users, which is
crucial for their business model of continuous content production and
acquisition.
- Uniyal, M. (2024). Bias and Variance in Machine Learning.
- Encord. (2025). Variance.
- Geeks for geeks. (2024). Bias and Variance in Machine Learning.
- Wickramasinghe, S. (2024). Bias–Variance Tradeoff in Machine Learning: Concepts & Tutorials.
- Iterate. (2025). Variance: The Definition, Use Case, and Relevance for Enterprises.