A Step-by-Step Guide to Image Classification of the IRIS Dataset with Hyperparameter Tuning

The Iris dataset is one of the most well-known examples in machine learning. It contains measurements of iris flowers and is often used to teach classification techniques. While the original dataset is made up of numerical features such as petal length and width, it can also be presented visually in image form, offering a different perspective on how models can be trained to classify data. In this article, we explore how to classify images derived from the Iris dataset and discuss how hyperparameters influence the process. You’ll see how models are trained step by step, along with explanations of key concepts.

Step by Step: Classify Images of the IRIS Dataset and Hyperparameters

Preparing the Iris Dataset as Images

The Iris dataset has just 150 entries, each describing a flower from one of three species: Setosa, Versicolor, or Virginica. While it’s usually worked with as rows of numbers, you can make it more visually interesting by turning those numbers into images. A simple way to do this is by plotting feature combinations—like petal length against width—as scatter plots or grayscale grids, then saving each plot as an image file.

Each image carries the correct species label, so the task becomes image classification rather than working with tables. To make the images suitable for training, they’re often resized to something like 28×28 pixels in grayscale. After creating the full set of labeled images, you split them into training, validation, and test sets, which helps your model learn effectively and perform well on unseen data.

Choosing a Model for Image Classification

Once the image dataset is ready, the next step is to choose a model. For simple datasets like this, even a basic convolutional neural network (CNN) performs well. A CNN is a type of neural network specifically designed to work with image data. It uses filters that slide over the image to pick up patterns, such as edges, shapes, and textures, which help it understand and classify images.

For this task, the CNN can have an input layer matching the image size (e.g., 28×28 pixels), followed by one or two convolutional layers with pooling layers to reduce dimensionality. Then, fully connected layers lead to an output layer with three neurons, one for each species. The softmax function is usually applied at the output to turn the raw outputs into probabilities.

Training the Model

With the model architecture defined, the next step is to train it. During training, the model sees batches of images, makes predictions, and adjusts its internal weights to improve. The goal is to minimize the difference between its predictions and the true labels. This is done using a loss function like categorical cross-entropy.

The dataset is fed into the model for several iterations, called epochs. After each epoch, the model’s performance is checked on the validation set to monitor whether it’s improving or overfitting. A model that performs very well on training data but poorly on validation data is said to overfit, and adjustments are needed.

The Role of Hyperparameters

Hyperparameters are settings that control how the model learns. They are not learned from the data but are chosen before training starts. Picking good hyperparameters often has a big impact on the model’s performance. In image classification tasks like this, some of the most important hyperparameters include:

• Learning rate: This determines how much the model updates its weights at each step. A rate that’s too high can make the model jump around without settling, while a rate that’s too low can make learning very slow.
• Batch size: This is the number of images the model processes at once. A small batch size can make the model train noisily but may help it generalize better, while a large batch size can make training smoother but sometimes less effective.
• Number of epochs: This defines how many times the model sees the entire training set. Too few, and the model may not learn enough. Too many, and it might overfit.
• Number of layers and neurons: The complexity of the CNN, such as how many convolutional layers or how many neurons per layer, needs to match the difficulty of the task. For the Iris dataset, which is relatively simple, a shallow network usually works well.
• Optimizer: Algorithms like Adam, SGD, or RMSprop decide how the model updates its weights during training. Different optimizers have different characteristics and can affect how fast and effectively the model learns.

Experimenting with these hyperparameters and using techniques like grid search or random search can help find a combination that performs best.

Evaluating and Interpreting Results

Once training is complete, the model’s accuracy is measured on the test set. Accuracy shows what percentage of test images the model classified correctly. A confusion matrix can also be used to see how many images of each species were correctly classified versus misclassified. For the Iris dataset, good models can reach high accuracy since the data is well-behaved.

Visualization of feature maps and learned filters in the CNN can also give insight into what patterns the model is using to make decisions. For example, it may focus on the shape or spread of points in the scatter plot images.

Improving the Model

If the model doesn’t perform well, several steps can help. More data augmentation—such as rotating or flipping images—can make the model more robust. Regularization techniques like dropout, which randomly disable neurons during training, can help reduce overfitting. Tuning hyperparameters systematically can also bring noticeable improvements. Trying different model architectures, like deeper networks or different activation functions, may improve results further.

In some cases, transfer learning—using a model pre-trained on a large dataset—can also help. However, the Iris dataset is simple enough that a model trained from scratch is usually sufficient.

Conclusion

Classifying images of the Iris dataset helps illustrate how image classification works and how hyperparameters affect model performance. Converting numerical data into images enables convolutional neural networks to recognize patterns and predict species accurately. Each step, from data preparation to tuning, influences learning and generalization. This simple yet effective example demonstrates machine learning’s potential in image tasks and builds a strong foundation for tackling more advanced projects confidently.