3 Types of Errors in Machine Learning that You Should Know!

Machine learning is a branch of Artificial Intelligence that allows machines to analyze data and make predictions. However, if the machine learning model is insufficiently accurate, it may make errors in predictions. An error is a metric that measures how well an algorithm can predict a previously unknown dataset. Based on the errors, the machine learning model that can perform the best on the given dataset is chosen.

These errors will always exist in machine learning because there will always be a minor gap between model predictions and actual predictions. The primary goal of machine learning is to eliminate these errors so that more accurate results can be obtained.

Therefore, it is highly important to make prediction errors in advance. The prediction error for any machine learning algorithm can be categorized into 3 types: bias, variance, and irreducible error.

Bias Error

When a model makes predictions, a difference between the model's prediction values and actual/expected values arises, and this difference is known as a bias error. It is the incapacity of machine learning algorithms like Linear Regression to grasp the true relationship between data points. Because bias arises from assumptions in the model, each algorithm starts with some bias, making the target function straightforward to learn.

A model with low bias will make fewer assumptions about the target function's shape. Furthermore, a model with a high bias makes more assumptions, making it impossible to capture the key aspects of our dataset. A model with a high bias cannot perform effectively on new data.

Machine learning algorithms with low bias are Decision Trees, k-Nearest Neighbours and Support Vector Machines. On the other hand, algorithms with high bias are Linear Regression, Linear Discriminant Analysis and Logistic Regression.

Variance Error

If alternative training data were utilized, the variance would specify the amount of variation in the prediction. Variance describes how far a random variable is different from its predicted value. A model should ideally not differ significantly from one training dataset to the next, implying that the method should be capable of deducing the underlying mapping between input and output variables. There are two types of variance errors: low variance and high variance.

Low variance means that with changes in the training data set, there is a minor variation in the prediction of the target function. High variance, on the other hand, shows a high variation in the target function prediction with changes in the training dataset.

Machine learning algorithms with low variance are Linear Regression, Logistic Regression, and Linear discriminant analysis; whilst algorithms with high variance are decision tree, Support Vector Machine, and K-nearest neighbours.

Irreducible Error

Practices that apply to the development, deployment, delivery, and support of certain services in an organization. It includes business analysis, service design and desk, availability management, and so on.

Some of the model error cannot be ascribed to bias or variance. This irreducible error can for example be random noise, which is always present in a randomly initialized machine learning model. Irreducible error is the error that cannot be reduced irrespective of the models. It is a measure of the amount of Bias and variance cannot account for all the model errors. Random noise, which is always present in a randomly started machine learning model, is one example of irreducible error. Irreducible error is a type of error that cannot be reduced using any model. It is a metric for how much noise there is in our data because of unknown variables.

Technical Management Practices

If we wish to lessen the impact of model bias, we can use a low-bias machine learning technique, which increases model complexity and sensitivity. If we wish to limit model sensitivity to data changes, we can use a more rigid machine learning approach. The machine learning model cannot be free of irreducible mistake.

Conclusion

Machine learning model predictions allow businesses to make predictions as to the likely outcomes of a question based on historical data. Therefore, businesses should really need to understand the 3 types of errors in machine learning to be able to produce highly accurate predictions.

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Reference:

Brownlee, J. (2019, October 25). Gentle Introduction to the Bias-Variance Trade-Off in Machine Learning. Machine Learning Mastery. https://machinelearningmastery.com/gentle-introduction-to-the-bias-variance-trade-off-in-machine-learning/

C. (2020, November 2). Machine Learning Error: Bias, Variance and Irreducible Error with Python –. MachineCurve. https://www.machinecurve.com/index.php/2020/11/02/machine-learning-error-bias-variance-and-irreducible-error-with-python/

JavaTPoint. (n.d.). Bias and Variance in Machine Learning - Javatpoint. Www.Javatpoint.Com. https://www.javatpoint.com/bias-and-variance-in-machine-learning