Practical Comparison Between Neural Networks and Traditional Machine Learning on the Classic Iris Dataset

[Introduction] This project is the IrisClassifier project published by Mattex125 on GitHub (link: https://github.com/Mattex125/IrisClassifier, published on June 5, 2026). Its core goal is to compare the performance of random forest (a traditional machine learning algorithm) and PyTorch-based neural networks on the multi-class classification task of the classic Iris dataset, demonstrating the complete workflow of data preprocessing, model training, and evaluation. It has strong educational value for machine learning.

The Iris dataset was first used by British statistician Ronald Fisher in 1936, with data collected by Edgar Anderson. It contains 50 samples for each of the three Iris species (Iris setosa, Iris versicolor, Iris virginica), measuring four features: sepal length, sepal width, petal length, and petal width. Due to its moderate feature dimensions, balanced classes, high data quality, and clear classification boundaries, this dataset has become an ideal introductory dataset for machine learning beginners.

Key details of data preprocessing: 1. Stratified sampling: Use the stratify=y parameter when splitting the training and test sets to ensure the proportion of the three classes in both sets is consistent with the original data, avoiding class distribution imbalance that affects generalization evaluation; 2. Data standardization and leakage prevention: Calculate the mean and standard deviation only on the training set (fit_transform), and use the training set parameters to transform the test set (transform) to prevent test set information leakage leading to overly optimistic performance estimates.

The random forest classifier was implemented using scikit-learn. Even with only one decision tree, the test set accuracy reached 94.74%. The classification report shows: Class 0 (Iris setosa) has a precision and recall of 1.00 each; Class 1 (Iris versicolor) has a precision of 0.86 and recall of 1.00; Class 2 (Iris virginica) has a precision of 1.00 and recall of 0.85. The confusion matrix reveals that the model mainly confuses Class 1 and Class 2, which aligns with the known overlapping feature space of the dataset.

A feedforward neural network was implemented using PyTorch. The typical architecture includes: an input layer with 4 neurons (corresponding to the 4 features), a hidden layer (ReLU activation to introduce non-linearity), and an output layer with 3 neurons (corresponding to the 3 classes, Softmax activation). Cross-entropy loss is used as the loss function, and Adam or SGD are commonly used as optimizers, reflecting the standard paradigm for neural network classification problems.

Practical insights: 1. Model complexity should match the problem: On the Iris dataset, a simple random forest performs nearly perfectly, while complex neural networks have diminishing marginal returns. In practical applications, one should start with simple models; 2. Data preprocessing is as important as model selection: Stratified sampling and leakage prevention details determine deployment performance; 3. Comparative experiments help understand algorithms: Forming technical intuition by comparing performance, training speed, etc.

This project covers the complete machine learning lifecycle (data exploration, preprocessing, model training and evaluation comparison), making it an excellent reference for beginners to solidify their foundations. Extended thinking: When choosing algorithms in practical business scenarios, in addition to accuracy, indicators such as inference speed, interpretability, and deployment cost need to be considered. How to balance these factors?