With the rise of big data, supervised learning has become critical for industries such as finance, healthcare, and e-commerce. To appreciate exactly why it has gained such importance, let’s first understand what supervised learning is. In simple terms, supervised learning is a standard machine learning technique that involves training a model with labeled data. This blog will explain the fundamentals of supervised learning, its types, algorithms, and applications. We will also go over the steps involved in implementing supervised learning and some of the challenges that come with it.
What is Supervised Learning?
Supervised learning is a type of machine learning in which a computer algorithm learns to make predictions or decisions based on labeled data. Labeled data is made up of previously known input variables (also known as features) and output variables (also known as labels). By analyzing patterns and relationships between input and output variables in labeled data, the algorithm learns to make predictions. Image and speech recognition, recommendation systems, and fraud detection are all examples of how supervised learning is used. The examples below will help explain what supervised learning is.
3 Examples of Supervised Learning
Email Filtering
Supervised learning is commonly used in email filtering to classify incoming emails as spam or legitimate. A machine learning algorithm is trained using a labeled dataset containing examples of both spam and legitimate emails. The algorithm then extracts relevant information from each email, such as the sender’s information, the subject, the message body, and so on. It learns from the labeled dataset to identify patterns and relationships between these features and their corresponding labels (spam or legitimate). Once trained, the algorithm can use the extracted features to predict the label of new, unseen emails. If an email is predicted to be spam, it can be automatically filtered into a spam folder, saving the user’s inbox space.
Credit Scoring
In credit scoring, supervised learning is used to predict the creditworthiness of loan applicants. A labeled dataset containing examples of past loan applicants and their credit history, income, employment status, and other relevant factors is used to train a machine learning algorithm. The algorithm learns to recognize patterns and relationships between these features and their corresponding labels, such as whether or not the loan was repaid. Once trained, the algorithm can predict loan repayment likelihood for new loan applicants based on their input features.
Voice Recognition
Supervised learning is utilized in voice recognition to help virtual assistants and other applications recognize and understand spoken commands. A labeled dataset of spoken words and phrases with corresponding text transcripts is used to train a machine learning algorithm in such scenarios. The algorithm learns to recognize relationships between spoken word audio features such as pitch, amplitude, and frequency and their textual representations from the labeled dataset. Following the training phase, the algorithm can begin analyzing new audio inputs and attempting to transcribe them into text form. This allows virtual assistants to understand and respond to spoken commands like managing reminders, playing music, or controlling smart home devices.
What are the Types of Supervised Learning?
Regression
Regression is a supervised learning method for determining the relationship between dependent and independent variables. In addition, it employs labeled datasets in an algorithm to forecast continuous output for various data. Here, it is widely used in situations where the output must be a single value, such as weight or height. There are two types of regression:
Linear regression: This is used to detect the relationship between two variables and to make future predictions. It is further subdivided according to the number of independent and dependent variables. Simple linear regression, for example, is used when there is only one independent and one dependent variable. Multiple linear regression is used when there are two or more independent and dependent variables.
Logistic regression: Logistic regression is used when the dependent variable is categorical or has binary outputs such as ‘yes’ or ‘no’. Since logistic regression is used to solve binary classification problems, it predicts discrete values for variables.
Naive Bayes
The Naive Bayes algorithm is well-suited for large datasets because each program in the algorithm operates independently, and the presence of one feature has no effect on the other. Its applications include text classification, and recommendation systems, among others. There are various Naive Bayes models of which the decision tree is commonly used in business. A decision tree, unlike a flowchart, is a supervised learning algorithm composed of control statements containing decisions and their consequences. Iterative Dichotomiser 3 (ID3) and Classification algorithm and Regression Trees (CART) are two popular decision tree algorithms used in a variety of industries.
Classification
Classification is a type of supervised learning algorithm which involves the process of accurately assigning data to different categories or classes. In essence, it entails identifying and analyzing specific entities in order to determine the appropriate category or class. K-nearest neighbor, Random forest, Support vector machines, Decision trees, and Linear classifiers are some popular classification algorithms.
Neutral Networks
Neutral Networks perform the process of grouping or categorizing raw data. Additionally, this algorithm is also employed in the interpretation of sensory data and the identification of patterns. The algorithm’s use, however, is limited due to the need for high computational resources.
Random Forest
The random forest algorithm is known as an ensemble method as it combines multiple supervised learning techniques to make a conclusion. Moreover, it uses several decision trees to classify each tree, making it a popular choice in a variety of industries.
Steps Involved in Supervised Learning
The following are some of the common steps involved in supervised learning:
Gather labeled data
Divide the data into two sets: Training and Testing
Select an appropriate algorithm
On the training set, train the algorithm
Analyze the algorithm’s performance on the testing set
If necessary, fine-tune the model to improve performance
Make predictions on new, unlabeled data using the trained model
Advantages and Disadvantages
When implemented in a professional context, supervised learning can foster a healthy workplace environment that prioritizes ongoing education and supports a culture of continuous growth.
Some of its chief advantages include:
It gathers previous data, which aids in learning from past mistakes
It is a powerful Artificial Intelligence (AI) tool that can handle a wide range of business functions on its own
It is a reliable algorithm
Some of the drawbacks of supervised learning are:
Large data sets tend to be difficult to categorize
To operate, a certain level of expertise is required
It takes a long time to process