Dataset Structure: Features and Labels

In machine learning, every dataset is divided into two main parts:

• Features (X) — input variables used by the model to make predictions (e.g., age, height, or number of purchases).
• Labels (y) — the target variable the model is trying to predict (e.g., whether an email is spam or the price of a house).

In supervised learning, the model learns the relationship between features and labels to make accurate predictions.

Scikit-learn includes several built-in datasets for experimentation. One of the most commonly used is the Iris dataset, which contains measurements of iris flower species.

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from sklearn.datasets import load_iris

iris = load_iris()

# Features (X) - shape: (samples, features)
X = iris.data
print("Feature shape:", X.shape)
print("First row of features:", X[0])

# Labels (y) - shape: (samples,)
y = iris.target
print("Label shape:", y.shape)
print("First label:", y[0])

You can check the feature and target names to understand what each column and label represents:

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print("Feature names:", iris.feature_names)
print("Target names:", iris.target_names)

The following are some key points about features and labels:

• Features are the information your model uses to make predictions.

• Labels define the correct answers during training.

• X: input features, 2D array shape (n_samples, n_features).

• y: target labels, 1D array shape (n_samples,).

Organizing data correctly into X and y is essential for Scikit-learn functions like train_test_split() and .fit(). Proper separation of features and labels is the first step in preparing data for training.