What Are Hyperparameters In Machine Learning?

What Are Hyperparameters in Machine Learning?

If you've explored machine learning, you've likely encountered the term "hyperparameters." While it may sound complex, hyperparameters are straightforward and crucial to understanding how ML models function. What are they, and why do they matter? Let's break it down.

Parameters vs. Hyperparameters: The Basics

To get to hyperparameters, we first need to talk about parameters. In machine learning, parameters are the settings a model learns from the data you feed it. Think of a simple linear regression model—the kind that predicts, say, house prices based on square footage. The model learns a slope (how much the price changes per square foot) and a y-intercept (a baseline price). Those are parameters. They’re automatically tuned during training as the model crunches through examples. In a neural network, the parameters are called "weights" and “biases.”

Hyperparameters, on the other hand, are not learned parameters. They’re static. They’re the knobs and dials you set before the model starts learning. They don’t come from the data—they come from you, the human steering the ship. Hyperparameters control how the model learns, not what it learns.

Here’s a metaphor. Imagine a guitarist:

• Hyperparameters are the guitarist’s practice routine: how many hours they rehearse each day, the exercises they drill (e.g., scales, arpeggios, or chord progressions), and the tempo they set for practice (fast or slow). They tweak this routine—say, adding more fingerpicking drills or shortening sessions—if their playing isn’t sharp enough.

• Parameters are the muscle memory they build: the instinctive way their fingers hit every note, the rhythm they lock into, and the finesse of their strumming. Once mastered, this muscle memory lets them pick up a new piece of sheet music—like a song they’ve never played—and perform it flawlessly on the spot

In this metaphor, a new piece of sheet music is like a prompt for an LLM. It may be a unique, never-before-seen combination of words. But a well-trained LLM has seen so many similar or related prompts that any new prompt is familiar enough to create a sensible output.

A Real-World Example

Let’s make this more concrete with a neural network example. Imagine you’re training a deep learning model to classify handwritten digits from the famous MNIST dataset.

Here are some hyperparameters you might adjust:

• Learning Rate (α): Determines how much the model's weights change after each training step. A high learning rate might cause the model to converge too quickly (or never stabilize), while a low one might make training excruciatingly slow.

• Number of Hidden Layers: Defines the depth of your neural network. Too few layers might not capture complex patterns, while too many might lead to overfitting.

• Number of Neurons per Layer: Controls the model's ability to learn abstract representations. More neurons generally mean a more expressive model, but also more computational cost.

• Batch Size: Determines how many samples the model processes before updating weights. A small batch size makes learning noisy but can help escape local minima, while a large batch size stabilizes learning but may settle into suboptimal solutions.

• Number of Epochs: The number of times the model sees the entire dataset. Too few epochs might leave the model undertrained, while too many might lead to memorization instead of generalization.

For example, if we set:

• Learning Rate = 0.01

• 3 Hidden Layers with 128 Neurons Each

• Batch Size = 32

• 10 Epochs

These values define how the neural network learns, but they aren't derived from the data itself—they're chosen by us. If we find that our model is overfitting, we might add dropout layers (another hyperparameter) or reduce the number of neurons.

Why Hyperparameters Matter

Hyperparameters can make or break your model. Choose the wrong ones, and your model might underperform—either failing to capture patterns (underfitting) or memorizing the training data so well it flops on new data (overfitting). Pick the right ones, and you’ve got a model that generalizes beautifully.

But there’s a catch: there’s no universal “best” set of hyperparameters. What works for a transformer generating coherent text might tank for a classifier identifying spam emails. It depends on the algorithm, the data, and what you’re trying to achieve.

How Do You Pick Them?

This is the million-dollar question. Finding the perfect hyperparameters can unlock a model’s full potential—or doom it to mediocrity—making it a high-stakes puzzle that researchers and practitioners obsess over. In the early days of ML, people set hyperparameters based on intuition or trial and error. Today, we’ve got smarter ways:

• Grid Search: Test every combination in a predefined range—like trying every possible learning rate from 0.001 to 0.1 and every batch size from 16 to 128. It’s thorough but slow.

• Random Search: Pick random combinations to test. Surprisingly, this can be faster and just as effective as grid search.

• Advanced Methods: Tools like Bayesian optimization or libraries like Optuna intelligently predict the next best settings based on previous attempts. Unlike grid search, Bayesian optimization uses past results to guess the next best settings, saving time.

You typically evaluate these options using a validation set—a chunk of data the model doesn’t train on—to see which combo performs best.

Hyperparameters in the Wild

Different ML algorithms have their own hyperparameters. For a neural network, you might tweak the learning rate (how fast it adjusts weights), the number of layers, or the number of neurons per layer. For a support vector machine, you might adjust the "C" parameter (how much to penalize misclassifications) or the kernel type. Each algorithm has its own flavor, but the core idea stays the same: hyperparameters shape the learning process.

The Takeaway

What exactly are hyperparameters in machine learning? They’re the controls you set before training begins, determining how a model processes and learns from data. Unlike parameters, which the algorithm dials in itself, hyperparameters come from you—they’re your instructions saying, “This is how you’ll tackle the task.” Mastering them blends intuition with rigor, a mix that drives machine learning’s power and, at times, its challenges.

When someone talks about tuning hyperparameters, they’re not just throwing out buzzwords—they’re shaping the process that refines the model’s parameters. In machine learning, those decisions dictate whether the model falters on unseen data or delivers precise predictions.