Demystifying Entropy: Its Critical Role in Machine Learning
Artificial Intelligence & Machine Learning
Machine learning is one of the most important AI-related technologies that enable machines and algorithms to learn from past experiences and data and improve themselves over time. And entropy is, without a doubt, one of the fundamental concepts of machine learning. What is entropy in machine learning, and why is it important? That’s what we want to discuss in this article.The truth is that entropy in machine learning is present in the majority of machine learning algorithms, especially decision trees and random forests. To understand what it is and how it works, let’s go back to the original meaning of this term.
What is entropy in physics?
Entropy is a term that originally comes from physics, specifically – thermodynamics. It describes disorder or randomness. In simple terms, entropy is a way of telling how disorganized something is. Things and systems in the universe tend to naturally move towards higher entropy over time, meaning they become more disordered, and entropy explains this phenomenon.
Entropy in machine learning
Entropy works similarly with machine learning. Here, it’s a measure of disorder within a dataset that’s used to fuel a machine learning algorithm. Let’s use a short example. Imagine you have a set of data with different classes and categories. Thanks to entropy, you can classify the uncertainty associated with predicting the class label of a random sample drawn from this dataset. The higher the entropy level, the greater the uncertainty. In other words, the lower the entropy is, the more predictable or organized a given dataset is.
The number of classes and categories is also important. Entropy reaches its minimum (zero) when all samples in the dataset belong to just one class because then we have something that could be called the perfect order.
Now, let’s have a look at how entropy works with different ML techniques:
ENTROPY IN DECISION TREES
Decision trees are very common in classification tasks. Here, entropy serves as a metric for decision tree algorithms that enables the selection of optimal split points. When we’re making a decision tree, the goal is to make each step as clear as possible. This means trying to make the different options as different from each other as we can. We use something called “information gain” to measure how much clearer the options become with each step. We look for the feature that makes the options the clearest, and we use that to decide how to split them up. And entropy plays a major role in this process.
Consider a simple example – dividing emails into spam and non-spam. In order to do this, we need to take a look at different features of each email, such as the sender, the subject, and its content. That’s because we want to minimize entropy (disorder) at each node so that our classification is more accurate.
ENTROPY IN RANDOM FORESTS
Random forests use decision trees to improve their accuracy and performance by taking multiple factors into account. Instead of relying on just one factor (one decision tree), random forests gather multiple factors from different trees, where each tree makes its own suggestion (or prediction). Next, the random forest combines all these factors to make the best choice (or prediction). Here, each tree is a subset of the original dataset, and entropy guides the feature selection process, influencing the tree’s structure and decision boundaries.
In a random forest, multiple decision trees are grown independently, with each tree contributing to the final prediction through a “voting mechanism”. In most scenarios, the decision made based on multiple trees tends to be more reliable compared to individual decision trees.
Random forests are sometimes used in the healthcare sector during medical diagnosis. Here’s an example: suppose we have a patient and we want to predict the probability of several diseases, e.g., cancer and Parkinson’s. With entropy in place, we can identify the most important parameters that will help us achieve as accurate prediction/diagnosis as possible. And random forests enable us to provide diagnoses for diverse medical conditions (not just one like in decision trees).
ENTROPY IN INFORMATION GAIN
Lastly, we want to show you why entropy is important in information gain. This technique, in simple terms, is all about figuring out which question gives us the best clue or the most information about the answer. Information gain is closely associated with entropy as here, it quantifies the reduction in uncertainty achieved by splitting a dataset based on a specific attribute. Information gain is not a separate ML technique; it’s rather a principle or an approach to various machine learning algorithms, making them more accurate and useful.
In general, ML algorithms always seek to maximize information gain as it makes them more useful. Therefore, attributes that offer more information gain are considered more relevant/important.
Here’s an example – predicting customer churn. ML techniques algorithms that are guided by information gain can analyze customer data to identify key factors in the future customer churn. By focusing on attributes with high information gain, such as service usage patterns and customer demographics, companies can come up with better retention strategies to lower customer churn and improve customer satisfaction.
Wrapping up
Whether we talk about decision trees, random forests, or information gain calculations, entropy plays a big role in quantifying disorder and improving our model’s optimization. Thanks to entropy, machine learning specialists can design more effective algorithms, deal with complex classification tasks, and extract actionable insights from various datasets.If you’d like to know more about entropy, you can read more in the article “What is entropy in machine learning?” by Addepto.
