Dataset Structure: Features and Labels

In machine learning, a dataset is typically organized into:

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

A model learns the relationship between features and labels in supervised learning.

Scikit-learn provides built-in datasets for practice. One of the most famous is the Iris dataset.

Copy
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 inspect the feature and label names of the Iris dataset using the following code:

Copy
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.