ML Basics: What Is Bias and Variance in Machine Learning?

Artificial intelligence and machine learning lead the bandwagon of advanced technologies in today’s world.

Machine learning, a set of algorithms that are pre-trained on large-scale language models, helps users across a wide range of applications. Research indicates that ML applications are primarily used for extracting better-quality information from large-scale data, enhancing productivity, reducing costs, and extracting more value from a dataset.

With that said, machine learning models aren’t free of errors. In this course, you will learn about bias and variance in machine learning. Both are types of error in machine learning algorithms that must be balanced to achieve optimum outputs.

Read on as we explore machine learning, including errors and bias, how to reduce them, variance and its types, the bias-variance trade-off, and more. Let’s begin!

Introduction to Machine Learning

Machine learning plays an important role in modern businesses, with 57% utilizing it to enhance consumer experiences and 49% integrating it into marketing and sales strategies. This underscores its importance in driving innovation and efficiency across industries.

Much like humans interact with their environments to gather information and learn from it; computers can be taught to do the same. This is the role of machine learning in computing systems.

Machine learning is a computer’s way of learning from patterns and trends presented by data that it gathers over time. It is a set of algorithms that enable a computer to gather data and learn independently from the insights extracted from this data.

Some of the most common applications of machine learning include speech recognition, email filtering, recommendation systems (like Netflix or Spotify), and image recognition.

Errors in Machine Learning

A machine learning model has two main types of errors: reducible errors and irreducible errors.

Irreducible errors are the errors that cannot be removed from a machine learning model because they derive from unknown variables.

On the other hand, reducible errors are those whose values you can reduce further to help improve the model’s accuracy and performance. Reducible errors can be classified further into two:

• Bias: This is the error between the actual output and the predicted output generated by the ML model.
• Variance: This is the error that occurs when an ML model learns too well on a limited dataset, leading to incorrect predictions when new data is presented to it.

What Is Bias in Machine Learning?

A machine learning model works by identifying trends and patterns in the input data. These patterns help inform the ML model to make generalizations about several instances in the data. After training, the ML model then applies these patterns and trends to new data to test their accuracy.

Bias is the assumption that an ML model makes about the new data to be able to predict an outcome or answer a query. When this prediction or response is off the mark, it becomes a bias—an error.

A high level of bias underlines that the assumptions that the model made were too basic, leading it to miss the important details in the data.

Types of Bias in Machine Learning

There are four major types of bias in machine learning:

1.  Sample Bias

Source: ubiai.tools

Sampling bias occurs when the data used to train an ML model does not closely represent the actual scenario and contains too many human inputs, reducing the dataset’s objectivity. This causes the ML model to remain partial and generate results that do not reflect the actual scenario.

2.  Prejudice Bias

Prejudice bias occurs when cultural stereotypes enter the training dataset as a result of the humans involved in the ML model training process. Allusions to social class, nationality, race, gender, etc., can impact the model’s fairness during predictions and can generate results that are offensive.

3.  Confirmation Bias

Confirmation bias is a complex error. If the ML model users have a hypothesis in place already that they would like to confirm with an ML model, then it is possible that the modeling and training process of the algorithm may be impacted by this intention and get manipulated into generating the expected answer.

4.  Group Attribution Bias

Source: wallstreetmojo.com

This error occurs when the training dataset has asymmetrical representations that skew the actual scenarios, leading to erroneous predictions. For example, if the model is fed with a restricted dataset in which one gender always earns more than the other, then it will learn these falsehoods and make incorrect predictions.

How to Reduce Bias in Machine Learning?

There are five key ways you can use to reduce bias in your machine-learning models:

1.  Prioritize Data Diversity

It is important to train your ML models on diverse datasets to ensure that the scenarios in which they work reflect the real world as closely as possible. You can achieve this by frequently reviewing your training data and removing problematic data.

2.  Identify Edge Cases

Edge cases are scenarios that occur outside of the algorithm’s operating parameters. For example, a model may be trained to identify a pedestrian and an autonomously driving vehicle, but not a collision between the two.

For this reason, it is important to identify these edge cases and define the analysis protocol for the model to follow so that biases or accidents do not occur.

3.  Be Consistent and Accurate in Data Annotation

Errors in prediction stem from errors in the dataset. Remove any noise in your training dataset while ensuring that you don’t make any errors while annotating it. Even if a dataset on the whole has high accuracy, if the components of the dataset are not annotated with equally high accuracy, then there will be errors in predictions.

4.  Check Your Model Constantly

ML models are constantly learning and evolving with each new piece of data they gather. It is thus important to keep checking on the accuracy of your ML model from time to time. Ensure that you update the training dataset according to the latest information to make accurate predictions.

What Is Variance in Machine Learning?

Variance is the measure of deviation that a predicted value has from the expected or actual value. In the context of machine learning, variance is the measure of change in the model’s performance when it is trained on different datasets.

In other words, variance defines the sensitivity of an ML model to the input data. A low variance value means that the model is less sensitive to data and can generate consistent results. High variance highlights that a model is sensitive, and the predictions may change with each result.

Bias-Variance Tradeoff

Source: cristianefragata.medium.com

In a machine learning model, bias and variance are inversely related. The higher the bias of a model, the lower the variance. A model cannot have both bias and low variance.

Therefore, a bias-variance trade-off is the concept of achieving a balance between the two errors that leads to the most accurate results and predictions by the model.

For example, as a data engineer uses modified datasets to better fit a model to reduce bias, he will invariably introduce higher variance. In the same way, when he tries to reduce variance to reduce the risk of bad predictions, the model will not match the dataset properly, increasing bias.

The bias-variance trade-off can be tackled in multiple ways

• Increasing model complexity decreases bias while keeping variance below the threshold
• Increasing the training dataset decreases variances while keeping biases in check

Different Combinations of Bias-Variance

The table below highlights various combinations of bias and variance:

Wrapping Up

Machine learning algorithms are designed with precision and accuracy in mind. Errors should ideally have no place in the system, but bias and variance are inevitable. It is important to learn thoroughly about how bias and variance can be controlled and balanced to achieve an ML model that performs as expected and keeps improving with each new dataset.

These concepts aren’t difficult to understand, especially with IEEE Blended Learning’s simple and bite-sized modules on everything about machine learning.

Whether you are a student, an industry professional, an educator, or simply someone interested in machine learning, you should check out IEEE’s courses and blogs on machine learning today.

FAQs

1.  What do you mean by bias in machine learning?

Bias is the inability of a machine learning algorithm to capture the relationship between various data points in the dataset, resulting in inaccurate results.

2.  What is bias vs variance?

Bias and variance are irreducible errors in machine learning. A model cannot have both high or low bias and variance at the same time. If one is high, the other will be low. Achieving an acceptable balance between the two is called a bias-variance trade-off.

3.  What is machine bias?

Machine bias is the result of an incorrect assumption that an ML model makes. This is caused by either the underestimation or overestimation of the features or parameters of the training dataset.