IRIS

🌸 Iris Type Classification

Problem Description

The goal is to build a classification model that determines the species of a flower based on its characteristics.

Each sample is characterized by 4 numerical attributes: sepal and petal length and width, and the label (species) indicates the flower species:

• setosa
• versicolor
• virginica

This type of problem belongs to the multi-class classification category.

The dataset is derived from the classic Iris collection (UCI ML Repository).

🔹 Features

• sepal_length
• sepal_width
• petal_length
• petal_width

📘 Input File Structure

train.csv

Contains all 4 feature columns plus the column:

• target – represents the flower species (0=setosa, 1=versicolor, 2=virginica)

Example:

1     sepal_length_(cm)  sepal_width_(cm)  petal_length_(cm)  petal_width_(cm)  target  SampleID28                  4.4               2.9                1.4               0.2       0         93106                4.9               2.5                4.5               1.7       2       107476                 6.8               2.8                4.8               1.4       1        7759                  4.9               3.1                1.5               0.1       0        10689                 5.5               2.5                4.0               1.3       1        90

test.csv

Contains the same columns without target, but includes SampleID.

Example:

1     sepal_length_(cm)  sepal_width_(cm)  petal_length_(cm)  petal_width_(cm)  SampleID238                 4.4               3.0                1.3               0.2        393127                6.1               3.0                4.9               1.8       128457                 4.9               2.4                3.3               1.0        58593                 5.0               2.3                3.3               1.0        94642                 4.4               3.2                1.3               0.2        43

📤 Submission

The output file (submission.csv) must contain exactly two columns:

• SampleID
• label – predicted species (0, 1 or 2)

Example:

⚙️ Evaluation

Model evaluation will be performed using the following metric:

• Macro F1-score

This metric is suitable for multi-class classification because it gives equal weight to each class, regardless of the number of examples in each.

General formula:

where C is the number of classes, and F1_i is the F1 score for class i.

The final score is expressed as a percentage (0–100), rounded to two decimal places.

📊 Source

The dataset comes from the original collection:
UCI Machine Learning Repository – Iris Data Set