Machine learning (ML) is a powerful tool that enables computers to learn from data and make predictions or decisions. But not all machine learning is the same – there are different types of learning, each suitable for specific tasks. The two most common types are supervised learning and unsupervised learning. In this article, we'll explore the differences between them, provide real-world examples, and walk through code snippets to help you understand how they work.

What is supervised learning?

Supervised learning is a type of machine learning in which an algorithm learns from labeled data. In other words, the data you provide to the model includes input features and the correct outputs (labels). The goal is for the model to learn the relationship between inputs and outputs so that it can make accurate predictions on new, unseen data.

Real world examples of supervised learning

Email Spam Detection:

• Input: The text of the email.
• Output: Label indicating whether the email is "Spam" or "Not Spam".
• The model learns to classify emails based on labeled examples.

House Price Forecast:

• Input: Characteristics of the home (e.g. square footage, number of bedrooms, location).
• Output: Price of the house.
• The model learns to predict prices based on historical data.

Medical Diagnosis:

• Input: Patient data (e.g., symptoms, lab results).
• Output: Diagnosis (e.g. "Health" or "Diabetes").
• The model learns to diagnose based on labeled medical records.

What is unsupervised learning?

Unsupervised learning is a type of machine learning in which algorithms learn from unlabeled data. Unlike supervised learning, no correct output is provided. Instead, models try to find patterns, structures, or relationships in the data on their own.

Real world examples of unsupervised learning

Customer segmentation:

• Input: Customer data (e.g. age, purchase history, location).
• Output: Groups of similar customers (e.g., "high-frequency buyers", "budget shoppers").
• The model identifies clusters of customers with similar behavior.

Anomaly detection:

• Input: network traffic data.
• Output: Identify unusual patterns that may indicate a cyber attack.
• The model detects outliers or anomalies in the data.

Market Basket Analysis:

• Input: Grocery store transaction data.
• Output: Groups of products that are often purchased together (e.g., "bread and butter").
• The model identifies associations between products.

The main differences between supervised learning and unsupervised learning

Code Example

Let’s dig into some code and see how supervised and unsupervised learning work in practice. We will use Python and the popular Scikit-learn library.

Supervised Learning Example: Predicting House Prices

We will use a simple linear regression model to predict the price of a home based on characteristics such as square footage.

# 導(dǎo)入庫(kù)
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# 創(chuàng)建樣本數(shù)據(jù)集
data = {
    'SquareFootage': [1400, 1600, 1700, 1875, 1100, 1550, 2350, 2450, 1425, 1700],
    'Price': [245000, 312000, 279000, 308000, 199000, 219000, 405000, 324000, 319000, 255000]
}
df = pd.DataFrame(data)

# 特征 (X) 和標(biāo)簽 (y)
X = df[['SquareFootage']]
y = df['Price']

# 將數(shù)據(jù)分成訓(xùn)練集和測(cè)試集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 訓(xùn)練線性回歸模型
model = LinearRegression()
model.fit(X_train, y_train)

# 做出預(yù)測(cè)
y_pred = model.predict(X_test)

# 評(píng)估模型
mse = mean_squared_error(y_test, y_pred)
print(f"均方誤差：{mse:.2f}")

Unsupervised Learning Example: Customer Segmentation

We will use K-means clustering algorithm to group customers based on their age and spending habits.

# 導(dǎo)入庫(kù)
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

# 創(chuàng)建樣本數(shù)據(jù)集
data = {
    'Age': [25, 34, 22, 45, 32, 38, 41, 29, 35, 27],
    'SpendingScore': [30, 85, 20, 90, 50, 75, 80, 40, 60, 55]
}
df = pd.DataFrame(data)

# 特征 (X)
X = df[['Age', 'SpendingScore']]

# 訓(xùn)練 K 均值聚類模型
kmeans = KMeans(n_clusters=3, random_state=42)
df['Cluster'] = kmeans.fit_predict(X)

# 可視化集群
plt.scatter(df['Age'], df['SpendingScore'], c=df['Cluster'], cmap='viridis')
plt.xlabel('年齡')
plt.ylabel('消費(fèi)評(píng)分')
plt.title('客戶細(xì)分')
plt.show()

When to use supervised learning vs. unsupervised learning

When to use supervised learning:

• You have labeled data.
• You want to predict outcomes or classify data.
• Examples: Predicting sales, classifying images, detecting fraud.

When to use unsupervised learning:

• You have unlabeled data.
• You want to discover hidden patterns or structures.
• Examples: Group customers, reduce data dimensions, and find anomalies.

Conclusion

Supervised learning and unsupervised learning are two basic methods in machine learning, each with its own advantages and use cases. Supervised learning is great for making predictions when you have labeled data, while unsupervised learning is great when you want to explore and discover patterns in unlabeled data.

By understanding the differences and practicing with real-world examples, such as the ones in this article, you will master these basic machine learning techniques. If you have any questions or want to share your own experiences, please feel free to leave a comment below.

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