Introduction to supervised learning: real-life examples

Supervised learning is a key concept in the world of machine learning, powering many applications from email filtering to medical diagnosis. In this article, we'll walk you through the basics of supervised learning, provide real-life examples, and even go through coding examples using Python. Whether you are a beginner or an intermediate developer, this article will help you understand the basics of supervised learning.

Understand supervisory training

Supervised learning is a form of machine learning in which algorithms learn from labeled training data to make predictions or decisions about new, unseen data. The term "supervision" refers to the fact that the algorithm has a superintendent with specific data. In supervision, each data point in the training set is associated with the corresponding target output, allowing the algorithm to learn the relationship between inputs and outputs.

Real life example

Example 1: Estimate the value of the house

Imagine you're a real estate agent trying to predict a home's price based on various features, such as square footage, number of bedrooms, and layout. You can use supervised learning to build a model that examines the relationship between these characteristics and the actual sales price of the house. Once trained, the model can help potential buyers and sellers accurately price new homes based on their features.

Example 2: Medical diagnosis

Supervisory training is also used in the medical field. Let's say you're working on a project to determine whether a patient has a certain medical condition based on symptoms and test results. By training the model on a database of patients with specific diagnoses, the model can learn patterns that indicate the presence of the condition. When given new patient information, the model can predict whether the patient has the condition, helping doctors make informed decisions.

Example of coding: Predicting house prices

To demonstrate supervised learning in action, create a simple linear regression model to predict house prices using the Scikit-Learn Python library.

# Import required libraries
import numpy as np
sklearn.model_selection import train_test_split
LinearRegression from import sklearn.linear_model
import mean_squared_error from sklearn.metrics

# Copy data
features = np.array([[1400], [1600], [1800], [2000], [2200]])
value = np.array([200000, 220000, 250000, 280000, 300000])

# Share data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(feature, value, test_size=0.2, random_state=42)

# Create and train linear regression models
model = LinearRegression()
model.fit(X_train, y_train)

# Making assumptions in the test set
prediction = model.predict(X_test);

# Calculate the mean squared error
mse = mean_squared_error(y_test, estimate)
print (f "Mean Squared Error: {mse}")

Conclusion 😊

Supervised learning forms the basis for many machine learning applications that enable prediction, classification, and decision making based on specific data. As a beginner or intermediate developer, understanding how supervised learning works and implementing it using tools like Python's Scikit-Learn can be a rewarding experience.