Supervised machine learning – easy steps to begin regression or classification with Python code

Dive into supervised machine learning with these straightforward steps. Learn how to use models leveraging labeled data to make accurate predictions and classifications. Perfect for beginners looking to understand and implement supervised learning effectively.

Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today’s leading companies, such as Meta, Google, Netflix and Uber, make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies.

What are common ways in which machines learn?

Classical machine learning is often categorizes algorithms in the way it learns and predicts accurately. There are four basic approaches: supervised learning, unsupervised learning, semi-supervised learning / self-supervised learning and reinforcement learning. The type of algorithm data scientists choose to use depends on what type of data they want to predict.

Supervised learning

In this type of machine learning, data scientists supply algorithms with labeled training data and define the variables they want the algorithm to assess for correlations. Both the input and the output of the algorithm is specified.

Unsupervised learning

This type of machine learning involves algorithms that train on unlabeled data. The algorithm scans through data sets looking for any meaningful connection. The data that algorithms train on are predetermined while the predictions or recommendations they output are learned from the data.

Semi-supervised learning

This approach to machine learning involves a mix of the two preceding types. Data scientists may feed an algorithm mostly labeled training data, but the model is free to explore the data on its own and develop its own understanding of the data set.

Reinforcement learning

Data scientists typically use reinforcement learning to teach a machine to complete a multi-step process for which there are clearly defined rules. Data scientists program an algorithm to complete a task and give it positive or negative cues as it works out how to complete a task. But for the most part, the algorithm decides on its own what steps to take along the way.

Introduction to supervised machine learning

Supervised machine learning is a type of artificial intelligence that trains algorithms on labeled data to make predictions or take actions based on input data. It involves a model learning from past observations and making predictions on new, unseen data. The goal is to develop a model that can generalize from the training data to unseen data.

Supervised machine learning is a subfield of artificial intelligence where a model is trained on labeled data to make predictions or take actions based on new input data. It uses algorithms that can learn from the data and improve their predictions over time. The labeled data used in supervised learning includes input features and corresponding output labels, allowing the algorithm to learn the relationship between the inputs and outputs. This learning process helps the algorithm make accurate predictions on new data it has not seen before.

Supervised learning is used in a wide range of applications, such as image classification, speech recognition, sentiment analysis, and predictive maintenance. The success of a supervised learning model depends on the quality and size of the training data, as well as the choice of algorithm. Common algorithms used in supervised learning include linear regression, logistic regression, decision trees, and neural networks.

What is supervised learning?

As the name suggests, supervised learning involves training a computer system using labeled data. This means that each piece of data comes with a known correct answer. The system learns from these examples to make predictions or classifications on new, unlabeled data. Essentially, the machine is taught using a set of training examples, which it uses to analyze and accurately predict outcomes for new data.

In this instance, we have pictures labeled as “spoon” or “knife”. The machine receives this known data and processes it to assess and learn the correlation of the images based on their characteristics, such as size, shape, sharpness, etc. Now, using the historical data, the machine can properly predict that a fresh image fed to it is a spoon based on its characteristics. Thus, the machine learns the things from training data and then applies the knowledge to test data. 

Supervised machine learning requires the data scientist to train the algorithm with both labeled inputs and desired outputs. Supervised learning algorithms are good for the following tasks:

• Binary classification: Dividing data into two categories.
• Multi-class classification: Choosing between more than two types of answers.
• Regression modeling: Predicting continuous values.
• Ensembling: Combining the predictions of multiple machine learning models to produce an accurate prediction.

Supervised learning is classified into two categories of algorithms:

1. Classification
2. Regression

Want to get into a machine learning career? Read this post or enroll in our machine learning work experience program.

Introduction to classification as a supervised learning technique

Classification is a type of supervised machine learning where the model is trained to predict a categorical output. The output can be one of several pre-defined classes. It’s used for problems like spam detection, sentiment analysis, and image classification. The model is trained to learn the relationship between input features and the output class, allowing it to make predictions for new data.

The variable to be predicted has two or more classes and is categorical, say, true or false, male or female, yes or no, etc.

For example, to determine if an email is spam, we first need to train the computer to recognize what spam looks like. This is done by using spam filters that analyze the email’s header and body for suspicious patterns. These filters look for specific keywords and check against known blacklists of banned spammers. Based on these factors, the email is assigned a spam score. A lower spam score indicates a lower likelihood of the email being spam. The algorithm then uses this score, along with the content and labels, to decide whether new incoming emails should be placed in the inbox or the spam folder.

Get started immediately with classification using KNN – python example with scikit learn

from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# Load the digits dataset
digits = load_digits()
X = digits.data
y = digits.target

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Create and train the K-Nearest Neighbors classifier
model = KNeighborsClassifier()
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy:.2f}")

Introduction to regression as a supervised learning technique

Regression is a type of supervised machine learning that involves predicting a continuous output value. It’s used for problems like stock price prediction, housing price prediction, and weather prediction. The model is trained to learn the relationship between input features and the output value, allowing it to make predictions for new data.

The variable to be predicted is a real or continuous value. A change in one variable is related to a change in the other in this situation because there is a relationship between the two or more variables. For instance, regression can be used to predict the house price from training data that may include locality, size of a house, etc.

Regression example with simple explanation

Let’s take two variables: temperature and humidity. The independent variable in this situation is “temperature,” and the dependent variable is “humidity.” The humidity drops as the temperature rises.

The model is fed these two variables, and as a result, the computer learns how they relate to one another. Once trained, the system can accurately forecast the humidity depending on the temperature.

The following is another example of airfare vs distance.

Get started immediately with linear regression – python example with scikit learn

import numpy as np
from sklearn.datasets import load_diabetes
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# Load the diabetes dataset
diabetes = load_diabetes()
X = diabetes.data
y = diabetes.target

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

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

# Make predictions
y_pred = model.predict(X_test)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
print(f"Mean Squared Error: {mse:.2f}")

Applications of supervised learning

• Risk Assessment: In order to reduce the risk portfolio of the companies, supervised learning is used to analyze risk in the financial services or insurance domains.
• Image classification: One of the primary use cases for showing supervised machine learning is image categorization. For instance, Facebook can identify your friend in a photo from a collection of tagged images.
• Fraud Detection: To determine whether the user’s transactions are genuine or not. 
• Visual Recognition: The capacity of a machine learning model to recognize images, actions, places, people, and things.

Advantages: –

• Supervised learning allows collecting data and produces data output from previous experiences.
• Helps to optimize performance criteria with the help of experience.
• Supervised machine learning helps to solve various types of real-world computation problems.

Disadvantages: –

• Classifying big data can be challenging.
• Training for supervised learning needs a lot of computation time. So, it requires a lot of time.

Classification vs regression

Classification and regression are two types of supervised machine learning that are used to solve different types of problems. In classification, the goal is to predict a categorical output, while in regression, the goal is to predict a continuous output. The choice between the two depends on the nature of the problem and the type of output required.

Classification and regression are two common types of supervised machine learning. The main difference between them is the type of output they predict.

• Classification is a type of supervised machine learning that is used to predict a categorical output, such as a label or a class. The output can be one of several pre-defined classes, and the goal is to train a model that can accurately predict the class of new, unseen data. Examples of classification problems include image classification, spam detection, and sentiment analysis.
• Regression, on the other hand, is used to predict a continuous output, such as a numerical value. The goal is to train a model that can accurately predict the value of a continuous target variable based on input features. Examples of regression problems include stock price prediction, housing price prediction, and weather prediction.

The choice between classification and regression depends on the nature of the problem and the type of output required. If the goal is to predict a categorical output, then classification is the appropriate technique. If the goal is to predict a continuous output, then regression is the appropriate technique.

How to decide when to use regression or classification models?

Reference and further reading about supervised learning

• Outliers: Finding Them in Data, Formula, Examples. Easy Steps and Video – Statistics How To
• ML-powered anomaly detection for outliers – Amazon QuickSight
• https://www.section.io/engineering-education/complete-guide-to-dealing-with-missing-values-in-python

Frequently Asked Questions About Supervised Learning in Machine Learning