Bias vs. Variance in Machine Learning: What’s the Difference?

Key takeaways 

Bias and variance in machine learning reflect how the model responds to training data and its ability to make accurate predictions.

• High variance can be caused by irrelevant data in the training set, whereas bias is a result of a training set that's too simple.

• In models with high variance, overfitting can occur, whereas models with high bias are more prone to underfitting.

• You can effectively train your machine learning model by finding the ideal balance between bias and variance. 

Discover the differences between bias versus variance in machine learning data trends. If you’re interested in developing machine learning skills and learning about fundamental AI concepts, the Machine Learning Specialization from Stanford and DeepLearning.AI will give you the opportunity to build and train neural networks, apply supervised and unsupervised learning techniques, and use classification algorithms.

Bias vs. variance in machine learning: What’s the difference?

Several mechanisms allow models to predict trends, patterns, and insights. It’s understandable, then, that prediction errors occur. Bias and variance are prediction errors that happen in machine learning, and there is a tradeoff when trying to minimize the two. The key difference between bias and variance in machine learning is that bias occurs when the data leads to wrong assumptions. In contrast, variance occurs when there is high sensitivity to variation in the training data. Because data aims to be as accurate as possible, these errors can lead to potential wrongdoing when left unaddressed. 

What is bias in machine learning?

Bias in machine learning is an error that skews an algorithm’s result, either in favor of or against a particular concept in the data. It occurs due to an incorrect assumption made during the process. Bias exists between the model’s prediction and what is true. 

A machine learning model with high bias will not match the data set as closely. That means it might be unable to capture data trends, tend to be too simplified, and have a high error rate.

What is variance in machine learning?

Variance in machine learning is an error that occurs when the model’s prediction changes as it is trained on different subsets of the training data. It happens when very complex machine learning models have many features. Complicated models will have more variance.

When a machine learning model has high variance, it might be due to having “noise” in the data set (or data within the set that is corrupted, meaningless, or irrelevant). It may also happen when an engineer tries to place the data points too close to each other.

Is bias or variance overfitting?

When it comes to training machine learning models, the models with high variance are more prone to overfitting, while those with high bias are susceptible to underfitting. However, by reducing irrelevant training data, the model can better learn to make accurate predictions.

The bias vs. variance machine learning tradeoff explained. 

The bias-variance tradeoff refers to the inverse relationship between bias and variance in a machine learning model, and finding the right balance between them. It deals with how complex a model is, how accurate its predictions are, and how well the model makes predictions on data not used to train it. Generally, it’s desirable to have low error or bias, which happens when the model has more flexible parameters. The tradeoff is that flexible models have more variance each time samples are used to create new training sets, so a balance needs to be found. 

Models with high bias have low variance, and models with low bias have high variance.

Underfitting and overfitting explained.

Machine learning models with high variance and low bias accurately represent the data set but are prone to overfitting. 

Overfitting refers to instances in which the model attempts to match nonexistent data in its predictions. It happens when, in highly complex models, the model matches the data points but follows the training data sets too closely. However, in these cases, the model cannot pinpoint the same data point in new, unseen training sets and cannot predict an accurate outcome.

Models with high bias and low variance are prone to underfitting the training data set, to a simpler machine learning model that can’t register complexities in the data.

Underfitting is when the model cannot match all relevant points in the input data to the target data. It happens when the model isn’t complex enough to match the target data and is not following the training data closely enough due to its high bias.

In short, high bias leads to underfitting, and high variance leads to overfitting. Underfitting (bias) is negative because you don’t achieve the training or target data accuracy. Overfitting (variance) leads to instability and the inability to make accurate predictions on new data.

Learn more: Overfitting vs. Underfitting: What’s the Difference?

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