Types of Machine Learning: Supervised vs Unsupervised

Machine learning can be broadly divided into:

• Supervised Learning: Models learn from labeled data to make predictions.

• Unsupervised Learning: Models find patterns in unlabeled data.

Please refer to the slide deck for this lesson for a full conceptual breakdown.

Here, we'll focus on practical examples in Scikit-learn.

The following example shows how to use Scikit-learn to train a K-Nearest Neighbors classifier.

The K-Nearest Neighbors classifier is a simple classifier that predicts the class of a new data point based on the classes of the nearest data points in the training set.

Copy
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load dataset
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(
    iris.data, iris.target, test_size=0.2, random_state=42
)

# Create and train the model
model = KNeighborsClassifier(n_neighbors=3)
model.fit(X_train, y_train)

# Evaluate accuracy
print("Accuracy:", model.score(X_test, y_test))

The dataset used is the Iris dataset, which is a classic dataset for classification tasks.

The dataset contains 150 samples of iris flowers, with 4 features: sepal length, sepal width, petal length, and petal width.

The target variable is the species of the iris flower.

The following example shows how to use Scikit-learn to train a K-Means clustering model.

The K-Means clustering model is a simple clustering model that groups data points into clusters based on their similarity.

Copy
from sklearn.datasets import load_iris
from sklearn.cluster import KMeans

# Load dataset
iris = load_iris()
X = iris.data

# Create and fit the model
kmeans = KMeans(n_clusters=3, random_state=42)
kmeans.fit(X)

# Show first 10 cluster assignments
print("Cluster labels:", kmeans.labels_[:10])

• Supervised learning uses labeled data to predict outcomes.
• Unsupervised learning uses unlabeled data to discover patterns.
• Scikit-learn makes switching between the two approaches simple with a consistent API.