Bias vs Variance in Machine Learning: Difference Between Bias and Variance

A branch of artificial intelligence, machine learning allows machines to make predictions and analyse data. However, if the ML model is inaccurate, it can make prediction errors. These prediction errors are known as bias and variance. 

There is always a small difference between model and actual predictions. This is why these prediction errors – bias and variance – are constantly present. Bias and variance can be used in parameter tuning and predicting better-fitted models from the ones built. An analyst aims to reduce these errors to achieve a more accurate result. This blog will discuss bias vs variance machine learning in detail.

What Is Bias vs Variance Error in Machine Learning?

Bias and variance are both prediction errors in ML. Machine learning errors measure how precisely an algorithm can predict a previously unknown dataset. The machine learning model, best for that particular dataset, is selected based on these errors.

There are primarily two kinds of machine learning errors:

• Reducible errors: The model accuracy can be improved by reducing these errors. Bias and variance are a type of reducible error. 
• Irreducible errors: These will be present in the model regardless of the algorithm used. These errors are caused by unknown variables whose value cannot be reduced. 

The Significance of Bias and Variance

Before discussing bias and variance, let us understand the significance of the two. A model having balanced bias and variance has optimal generalisation performance. This implies that the model can capture the underlying patterns in the data without underfitting or overfitting. 

A trade-off exists between an ML model’s ability to minimise bias and variance. It is known as the best solution for choosing a value of regularisation constant. Properly understanding these errors helps avoid underfitting or overfitting the dataset. 

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Define Bias 

Generally, an ML model makes predictions by analysing the dataset and finding patterns. Using these patterns, generalisations can be made in the data. Once the model learns these patterns, it applies them to test the data for prediction. 

When making any predictions, a difference is observed between the actual values and prediction values made by the model. This difference is known as bias errors. Bias is a systematic error because of wrong assumptions in machine learning. 

Bias is the inability of machine learning algorithms to identify the true relationship between data points. Every algorithm starts with some bias since it occurs from assumptions in the model, making the target function simple to learn. 

A model has either of the two situations:

• Low bias – Low bias value implies fewer assumptions have been made to build the target function. In this scenario, the model will closely match the training dataset. 
• High bias – High bias value implies more assumptions have been made to build the target function. In this scenario, the model will not match the dataset closely. 

A high-bias model will be unable to capture the dataset trend. It has a high error rate and is considered an underfitting model. This happens because of a very simplified algorithm. For instance, a linear regression model might be biased if the data has a non-linear relationship. 

Ways To Reduce High Bias

Since we have discussed some disadvantages of having high bias, here are some ways to reduce high bias in machine learning. 

• Use a complex model: The extremely simplified model is the main cause of high bias. It is incapable of capturing the data complexity. In such scenarios, the model can be made more complex. 
• Increase the training data size: Increasing the training data size can help reduce bias. This is because the model is being provided with more examples to learn from the dataset. 
• Increase the features: Increasing the number of features will increase the complexity of the model. This improves the ability of the model to capture the underlying data patterns. 
• Reduce regularisation of the model: L1 and L2 regularisation can help prevent overfitting and improve the model’s generalisation ability. Reducing the regularisation or removing it completely can help improve the performance. 

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Definining Variance

Variance in machine learning can be described as the amount by which the performance of a predictive model alters when it is trained on various subsets of the training data. Variance is the model’s variability by sensitivity to another subset of the training dataset. 

In simple terms, variance can be defined as how much any random variable will differ from the expected value. Ideally, an ML model shouldn’t vary too much from one training dataset to another. The algorithm must understand the hidden mapping between input and output variables. 

Variance error is either low or high:

• Low variance: Low variance implies that the ML model is less sensitive to changes in the training data. The model will be able to produce consistent estimates for the target function using different data subsets of the same distribution. This is underfitting, where the model can’t generalise on test and training data. 
• High variance: High variance implies that the ML model is susceptible to changes in the training data. When trained on various subsets of data from the same distribution, the ML model can significantly change the target function estimation. This scenario is known as overfitting when the ML model does well on the training data but not on any new data. 

Ways To Reduce High Variance

Here are some ways high variance can be reduced:

• Feature selection: The variance error of a model can be reduced by selecting the only relevant feature. This will decrease the complexity of the model. 
• Cross-validation: By dividing the dataset into testing and training sets several times, cross-validation can identify if a model is underfitting or overfitting. This can be used for reducing variance by tuning the hyperparameters. 
• Simplifying the model: Decreasing the number of parameters of neural network layers can help reduce the complexity of the model. This, in turn, helps in reducing the variance of the model. 
• Ensemble methods: Boosting, stacking and bagging are common ensemble techniques that can help reduce the variance of an ML model and improve the generalisation performance. 
• Early stopping: This is a technique used for preventing overfitting by putting a stop to the deep learning model training when the validation set performance stops improving. 

Various Combinations of Bias-Variance

When talking about combinations between bias-variance, there are four main combinations. If you want to learn in-depth about these combinations, check out upGrad’s Executive PG Program in Data Science & Machine Learning from University of Maryland. 

The various combinations have been listed in the table below: 

Combination	Characteristic
High bias, low variance	This type of model is said to be underfitting.
High variance, low bias	This type of model is said to be overfitting.
High bias, high variance	This model cannot capture underlying patterns in the dataset (high bias) and is too sensitive to the training data changes (high variance). Due to this, the model will mostly give inaccurate and inconsistent predictions.
Low bias, low variance	This model type will capture the dataset’s underlying patterns (low bias) and isn’t very sensitive to the training data changes (low variance). This ML model is ideal as it can produce accurate and consistent predictions. However, it is not possible in practice.

Variance vs Bias in Machine Learning

The table below discusses the difference between bias and variance:

Bias	Variance
Bias occurs in a machine learning model when an algorithm is used but does not fit properly.	Variance is the amount of variation the target function estimation will change if different training data is used.
It is the difference between the actual values and the predicted values.	It talks about how much any random variable deviated from the expected value.
The model cannot find patterns in the training dataset, failing for unseen and seen data.	The model can find most patterns from the dataset. It learns from noise or unnecessary data.

How To Identify a Model Having High Variance or High Bias?

When trying to identify if a model has high variance or high bias, the following characteristics can help in making a difference:

Characteristics of a high-bias model are:

• Potential of underfitting 
• Unable to capture accurate data trends
• Extremely simplified
• High error rate

Characteristics of a high variance model are:

• Potential of overfitting
• Noise in the data set
• Trying to fit all data points as close as possible 
• Complex models

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What Is Bias-Variance Trade-Off?

It is impossible to have an ML model with low variance and low bias — variance and bias are inversely related. Data scientists who build ML models must decide about their models’ bias and variance levels. It is their responsibility to find the proper balance between the two. 

For getting the most accurate predictions, the goal is to get low bias and variance when building a machine learning algorithm. Data scientists also have to be careful about overfitting and underfitting. 

An ML model which shows small bias and high variance will overfit the target, whereas a model which shows high bias and small variance will underfit the target. 

A data scientist must create a flexible learning algorithm to fit the data to deal with trade-off challenges properly. However, another trade-off challenge is if the algorithm has too much built-in flexibility and might get too linear, providing results with high variance from every training data set. 

The trade-off can be tackled in a couple of ways:

• Increasing the model complexity: Increasing the complexity of an ML model decreases the overall bias, increasing the model’s variance to an acceptable level. This will align the model with the training dataset without having significant variance errors. 
• Increasing the training data set: This, to some extent, can also help balance the trade-off. It is the most preferred method when approaching overfitting models. This also allows users to increase the complexity of the model without any variance error polluting the model. 

A large data set gives more data points for the algorithm to generalise the data easily. 

However, the main issue is that low-bias or underfitting models are not very sensitive to the training data set. Hence, increasing data in the solution is preferred when dealing with high-bias and high-variance models. 

Conclusion

In machine learning, more work on bias and variance are in the making. It is difficult to build a model which has low bias and variance. The target is to create a model that reflects the training data linearity but will also be sensitive to unseen data for estimates and predictions. 

Data scientists must learn about the trade-off between bias and variance in machine learning to create a model with accurate results. 

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