How to Use AI Classification to Build More Efficient Apps

Artificial Intelligence has the power to revolutionize how we build and interact with technology, making our apps smarter, faster, and more intuitive.

In the first part of our AI blog series, we explored how regression can help you build more intelligent applications.

Now, we’re turning our attention to another powerful AI technique: Classification.

Imagine an AI that can instantly sort through your emails, categorize products in an online shop, or even diagnose medical conditions. Classification can make all those things happen by recognizing patterns and making quick decisions. 

In this post, we’ll delve into the fascinating world of classification, exploring the different types, key algorithms, and their practical applications. We’ll also show you a hands-on script that compares various classification algorithms using a specific real-world case.

What is classification?

Classification is a type of machine learning where the goal is to assign a label or category to a piece of data. Think of it as sorting things into different boxes based on their characteristics.

For example, when you receive an email, you might want to classify it as “spam” or “not spam.” That’s exactly what classification does — it helps the model decide which category (or “label”) the data belongs to.

In simple terms, classification is all about teaching a model to recognize patterns in data and then use those patterns to make decisions, like sorting emails or identifying whether an image contains a cat or a dog.

Unlike regression, which predicts a continuous value (like the temperature for tomorrow), classification is focused on predicting distinct categories (like “sunny,” “cloudy,” or “rainy”).

How does classification work?

Imagine you have a big basket of fruit and want to sort them into apples, oranges, and bananas. You’d first show the computer lots of examples of each fruit, telling it, “This is an apple,” “This is an orange,” and so on.

The computer then learns to recognize the differences — like the color, shape, and size — and uses this knowledge to sort new fruits it hasn’t seen before.

A developer’s perspective

In artificial intelligence, classification falls under supervised learning, meaning the algorithm learns from labeled data to predict outcomes. Building a classification model is like constructing a system that can make smart, data-based decisions. 

Here’s how the process usually works:

1. Data preparation: Just like sorting through a pile of mixed fruit, you start by cleaning and organizing your data. This step involves fixing errors, filling in missing information, and ensuring the data is in a format the model can understand. High-quality data is the key to a reliable model.
2. Model selection: Next, you choose the right tool for the job. If you’re working with straightforward data, Scikit-learn might be your go-to for traditional models like decision trees or logistic regression. Deep learning frameworks like TensorFlow or PyTorch are ideal for more complex tasks, especially those involving large amounts of data or images.
3. Training: This is where the model gets to work. You provide it with labeled data — examples where you already know the correct answers. The model then learns by adjusting its internal settings to minimize mistakes, like a student learning from practice questions.
4. Evaluation: After training, the model’s performance is put to the test. You check how well it can predict the right labels on a new set of data it hasn’t seen before. This step is crucial to ensure the model can handle real-world data, not just the examples it was trained on.
5. Deployment: After fine-tuning and ensuring the model performs well, it’s ready to be used in a real application — whether that’s categorizing emails, identifying objects in images, or any other classification task.

Classification types and their applications

AI classification is a powerful tool that helps businesses make smarter decisions by sorting data into meaningful categories.

Let’s explore the different types of classification and how they can be applied across various industries:

Binary classification

Binary classification deals with tasks that involve two distinct classes or categories. This is the most straightforward form of classification and is used when you need to make a clear, either-or decision.

Examples of binary classification include:

• Email spam detection: AI can sort emails into “spam” or “not spam” to keep your inbox clutter-free.
• Customer churn prediction: AI can predict whether customers are likely to leave (churn) or stay, letting you be proactive and reach out to customers who are more likely to churn.

Multi-class classification

Multi-class classification involves tasks with more than two categories to choose from. Instead of deciding between two options, the model selects from several possible categories.

Examples of multi-classification include:

• Plant species identification: AI can identify different species of plants by analyzing features like leaf shape and color.
• Product categorization in e-commerce: An online store can automatically categorize new products into existing categories like “electronics,” “clothing,” or “furniture.”

Multi-label classification

In multi-label classification, each data point can belong to more than one category at the same time. This type of classification is useful when an item can fit into multiple categories simultaneously.

Examples of multi-label classification include:

• Tagging photos: AI can tag a single image with multiple labels, like “beach,” “sunset,” and “vacation,” making it easier to organize and search for images.
• Document classification: A news article might be tagged under multiple topics like “politics,” “economy,” and “health,” reflecting its diverse content.

Imbalanced classification

Imbalanced classification is used when the categories are not evenly distributed, meaning one category has significantly more examples than others. This can make it challenging for the model to accurately predict the less common categories.

Examples of imbalance classification include:

• Medical diagnostics: In healthcare, AI can help identify rare diseases where positive cases are much less common than negative ones.
• Fraud detection in fintech: AI can detect fraudulent transactions, which are much rarer than legitimate ones.

Classification algorithms

When you’re building AI models, there are several key algorithms to choose from, each with its own strengths and applications. 

To get hands-on experience with the most popular algorithms, we’ve set up an interactive example using Google Colab.

In this example, we’ll take an exciting journey through the process of classifying handwritten digits using various machine-learning techniques.

Our goal is to understand how different models tackle the challenge of recognizing and categorizing digits and compare their performance.

You’ll learn how to train a computer to recognize handwritten numbers using the Scikit digits dataset, which is pre-processed and ready for model training. We’ll guide you through each step, from training the models to testing their accuracy.

Once the models are trained, we will compare them by printing the main evaluation metrics and plotting the confusion matrix.

What is a confusion matrix?

A confusion matrix is a powerful tool for evaluating the performance of a classification model. It shows how many predictions were made correctly and where the model got confused.

The matrix is a table that contrasts the actual labels with the predicted labels, providing insights into the types of errors the model is making. 

For example, in our digit classification example, a confusion matrix would reveal how often the model mistook a “2” for a “3” or correctly identified a “7.”

By analyzing the confusion matrix, we can understand the strengths and weaknesses of our model, helping us make necessary adjustments to improve accuracy.

Here’s a link to the Google Colab notebook where you can follow along and see the code in action: Google Colab Example

AI classification: accessible and effective

As we’ve seen, classification can be implemented in just a few steps, making it an accessible and powerful tool for making smarter decisions and automating critical processes.

From sorting emails and detecting fraud to understanding customer behavior and categorizing products, classification can be implemented in just a few steps, making it both accessible and incredibly effective.

However, building successful classification models involves more than just choosing the right algorithm. You’ll also need to navigate challenges like data quality, feature selection, and avoiding overfitting.

These hurdles are crucial to tackle if you want your classification models to be accurate, reliable, and truly beneficial for your business.

As you continue to explore and implement AI in your projects, remember that the potential of classification is only limited by the creativity with which you apply it.

Want to learn more app-building tips and tricks? Check out the rest of the Cheesecake Labs blog.

Are you interested in learning more about how your business can use AI techniques in your digital products? Send us a message. We’d love to chat about your ideas.