Sample paper
Word Count: approximately 2,300 words
Problem Description
The Iris flower dataset, first used by British statistician and biologist Ronald Fisher in 1936, comprises fifty samples each of three Iris species -- Iris setosa, Iris virginica, and Iris versicolor -- with four measured features: sepal length, sepal width, petal length, and petal width, all in centimetres. The exercise's goal is to build a machine learning model that predicts a flower's species from these four features, framing the task as a three-class classification problem. The dataset is a standard entry point for classification exercises: with 150 instances and four features, one class is linearly separable from the other two, while the remaining two classes present a modest additional challenge. The exercise proceeds from initial data exploration through visualisation, application of four classification algorithms, performance comparison, and hyperparameter tuning of the best-performing model.
Dataset Description
The dataset contains 150 instances split evenly across the three species (50 each), giving a balanced class distribution that avoids the bias imbalanced datasets can introduce toward majority classes. The four features -- sepallength, sepalwidth, petallength, and petalwidth -- are stored alongside the target species label in an Excel file, loaded using pandas' read_excel function, with features and target variable separated for modelling. The target variable is subsequently converted from categorical species names to numeric values, a standard requirement for most machine learning algorithms. Initial exploration covers dataset shape, feature summary statistics, and species counts to confirm the data is clean and balanced before modelling begins.
Choice of Algorithm
Four algorithms were selected to cover distinct classification approaches: Logistic Regression, a linear model that estimates class probability and performs well when classes are linearly separable; Decision Tree, an interpretable model that splits on features but can overfit without regularisation; Random Forest, an ensemble of many decision trees that introduces randomness in feature and sample selection to reduce overfitting relative to a single tree; and K-Nearest Neighbors, a distribution-free, instance-based method whose performance depends on selecting an appropriate value of k to avoid over- or under-fitting. Together these represent a linear model, a tree-based model, an ensemble method, and an instance-based method, allowing a broad comparison of approaches suited to this classification problem.
Description of Key Steps
The pipeline follows nine steps: visualising the data, splitting it into training and test sets, scaling features, training each model, generating predictions, evaluating performance, comparing models, examining feature importance, and tuning hyperparameters.
Data Visualisation
Pair plots, scatter plots, box plots, and violin plots were used to explore feature distributions and relationships across the three species. Pair plots show pairwise feature relationships with per-feature distributions on the diagonal; scatter plots highlight quartiles and outliers per feature; and violin plots, which combine a box plot with a kernel density estimate, show the distribution shape of each feature by species. Together these visualisations help surface trends, anomalies, and potential issues before classification begins.
Data Splitting, Scaling, and Model Training
The dataset was split into training and test sets using an 80/20 stratified split via scikit-learn's traintestsplit function, with a fixed random state for reproducibility and stratification to preserve class balance across both sets. Features were then scaled to prepare them for the classification algorithms. Each of the four models -- Logistic Regression, Decision Tree, Random Forest, and KNN -- was trained on the resulting training set, used to generate predictions on the test set, and evaluated individually using standard classification metrics before being compared as a group.
Feature Importance and Hyperparameter Tuning
A feature importance analysis using the Random Forest model identified petallength and petalwidth as the most influential features for species classification, with sepal_width contributing the least. Grid search was then used to tune the Random Forest model's hyperparameters, identifying a best configuration (Gini criterion, maximum depth of 4, 100 estimators) that achieved a 97.5% cross-validated accuracy during the search process.
Results Obtained
| Model | Test Accuracy | Notes |
|---|---|---|
| Logistic Regression | 93.3% | Balanced precision, recall, and F1-score across all three classes |
| Decision Tree | 90.0% | Misclassified one Iris Versicolor as Iris Virginica |
| Random Forest | 93.3% | No improvement over the individual Decision Tree in this case |
| K-Nearest Neighbors (k=5) | 93.3% | Misclassified two Iris Virginica as Iris Versicolor |
| Random Forest (tuned) | 90.0% | Matched pre-tuning performance; default hyperparameters were near-optimal |
All four models learned the underlying patterns in the Iris dataset and generalised well to unseen data. Logistic Regression, Decision Tree, and Random Forest performed comparably, while KNN performed marginally worse on some splits. The consistently high accuracies across models indicate that the Iris classification problem is relatively simple, with well-separated classes in feature space limiting the practical difference between algorithm choices.
Figures
!Figure 1: Feature importance bar chart comparing the relative contribution of sepal and petal measurements to Random Forest classification accuracy
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References
Fisher, R. A. (1988). Iris. UCI Machine Learning Repository. Fisher, 1988 Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(03), 90-95. McKinney, W. (2011). Pandas: A foundational Python library for data analysis and statistics. Python for High Performance and Scientific Computing, 14(9), 1-9. Pedregosa, F. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825. Waskom, M. (2024). seaborn: statistical data visualisation. Waskom, 2024