What are Bias and Variance in Machine Learning?

Machine learning deserves more scrutiny than ever due to the growing adoption of ML applications. The development and assessment of ML models have become more complex with the use of larger datasets, new learning requirements, innovative algorithms, and diverse implementation approaches. 

Therefore, it is important to pay attention to bias and variance in machine learning to ensure that machine learning models don’t make any false assumptions or get filled up with noise. Machine learning models must have the perfect balance between bias and variance to generate results with better accuracy.

In the development phase, all the algorithms would have some form of variance and bias. You can correct ML models for bias or variance, albeit without the possibility of reducing them to zero. Let us learn more about bias & variance alongside their implications for new machine-learning models.

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Why Should You Learn about Bias and Variance?

Before learning about bias and variance, it is important to figure out why you should learn the two concepts. ML algorithms rely on statistical or mathematical models that may feature two types of inherent errors, such as reducible errors and irreducible errors. Irreducible errors are naturally evident in an ML model, while reducible errors can be controlled and reduced to improve accuracy.

The elements of bias and variance in ML are perfect examples of reducible errors that you can control. Reduction of errors would demand selection of models with the desired flexibility and complexity alongside access to relevant training data. Therefore, data scientists and ML researchers must have an in-depth understanding of how bias is different from variance.

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Fundamental Explanation of Bias

Bias refers to the systematic error that emerges from wrong assumptions made by the ML model in the training process. You can also explain bias in machine learning in mathematical terms as the error emerging from squared bias. It represents the extent to which the prediction of an ML model is different when compared to the target value for specific training data. The origins of bias error revolve around simplification of assumptions within ML models for easier approximation of the end results.

Model selection is one of the reasons for introducing bias in ML models. Data scientists may also implement resampling to repeat the model development process and derive the average prediction outputs. Resampling of data focuses on extraction of new samples by leveraging datasets to achieve better accuracy in results. Some of the recommended methods for data resampling include bootstrapping and k-fold resampling. 

The overview of bias and variance in machine learning also points to the ways in which resampling could influence bias. ML models are likely to have a higher level of bias when average final results are not the same as the actual value in training data. All algorithms have some type of bias as they emerge from assumptions made by the model to learn the target function easily. Higher bias can result in underfitting as the model cannot capture the relationship between model features and outputs. High-bias models have more generalized perceptions about the end results or target functions.

Linear algorithms have a higher bias, thereby ensuring a faster learning process. Bias is the result of approximation of complicated real-life problems with a significantly simpler model in linear regression analysis. Even if linear algorithms can feature bias, it leads to easily comprehensible outputs. Simpler algorithms are more likely to introduce more bias than non-linear algorithms. 

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Fundamental Explanation of Variance 

Variance refers to the changes in the target functions or end result due to the use of disparate training data. The explanation for variance in machine learning also focuses on how it represents the variation of random variables from the expected value. You can measure variance by using a specific training set. It serves as a clear overview of the inconsistency in different predictions when you use diverse training sets. However, variance is not a trusted indicator of the overall accuracy of an ML algorithm.

Variance is generally responsible for overfitting, which leads to magnification of small variations in the dataset used for training. Models with higher variance could also have training datasets that showcase random noise rather than target functions. On top of it, the models can also determine the connections between output variables and input data.

Models with lower variance suggest that the sample data is closer to the desired state of the model. On the other hand, high-variance models are likely to showcase massive changes in the predictions for the target functions. Examples of high-variance models include k-nearest neighbors, decision trees, and SVMs or support vector machines. On the other hand, linear regression, linear discriminant analysis, and logistic regression models are examples of low-variance ML algorithms. 

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How Can You Reduce Bias in ML Algorithms?

The ideal way to fight against bias and variance in ML algorithms can help you create ML models with better performance. You can find different methods to address the problem of bias in ML models to improve accuracy. First of all, you can go for a more complex model. Oversimplification of the model is one of the common reasons for higher bias, as it could not capture the complexities in training data.

Therefore, you have to make the ML model more complex by reducing the number of hidden layers for deep neural networks. On the other hand, you can choose more complex models, such as recurrent neural networks for sequence learning and convolutional neural networks for image processing. Complex models such as polynomial regression models can serve as the ideal fit for non-linear datasets.

You can deal with bias in ML algorithms by increasing the number of features that would improve the complexity of ML models. As a result, it would have better abilities for capturing the underlying patterns you can find in the data. Furthermore, expanding the size of the training data for ML models can help in reducing bias as the model would have more examples for learning from the training datasets. 

Regularization of the model through techniques like L1 or L2 regularization can help in preventing overfitting alongside improving generalization features of the model. If you reduce the strength of regularization or remove it in a model with higher bias, then you can enhance its performance by huge margins. 

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How Can You Reduce Variance in ML Algorithms?

ML researchers and developers must also know the best practices to reduce variance in ML algorithms to achieve better performance. You can find a clear difference between bias and variance in machine learning by identifying the measures followed for reducing variance. The most common remedial measure for variance in ML algorithms is cross-validation.

It involves splitting the data into training and testing datasets many times for identification of overfitting or underfitting in a model. In addition, cross-validation can help in tuning hyperparameters for reduction of variance. Selection of the only relevant features can help in reducing complexity of the model, thereby reducing variance error. 

Reduction of model complexity through reduction of the number of layers or parameters in neural networks can help reduce variance and improve generalization performance. You can reduce variance in machine learning with the help of L1 or L2 regularization techniques. Researchers and developers can also rely on ensemble methods such as stacking, bagging, and boosting to enhance generalization performance and reduce variance.

Another trusted technique for reducing variance in ML algorithms is early stopping, which helps in preventing overfitting. It involves stopping the deep learning model training when you don’t find any improvement in performance on the validation set. 

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What is the Bias-Variance Tradeoff?

The discussions about bias and variance in machine learning also invite attention to bias-variance tradeoff. It is important to remember that bias and variance have an inverse relationship, thereby suggesting that you cannot have ML models with low bias and variance or high bias and variance. Data engineers working on ML algorithms to ensure alignment with a specific dataset can lead to lower bias, albeit with higher variance. As a result, the model would align with the dataset alongside improving possibilities of inaccuracy in predictions.

The same situation is applicable in scenarios where you create a low variance model that showcases higher bias. It may reduce the risk of inaccuracy in predictions, albeit with a lack of alignment between the model and the dataset. The bias-variance tradeoff refers to the balance between bias and variance. You can address the bias-variance tradeoff by increasing the training dataset and the complexity of the model. It is also important to remember that the type of model plays a major role in determining the tradeoff. 

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Final Words 

The review of the difference between bias and variance in machine learning shows that it is important to address these two factors before creating any ML algorithm. Variance and bias errors are major influences on the possibilities for overfitting and underfitting in machine learning. Therefore, the accuracy of ML models depends significantly on bias and variance. At the same time, it is also important to ensure the right balance between variance and bias. It can help you achieve better results from machine learning algorithms. Discover more insights on bias and variance to understand their importance now.

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