Supervised Learning in Machine Learning
In this article, we will cover another of the most important concepts of Machine Learning algorithms- Supervised Machine Learning. As you have noticed that this type of learning does have much application, therefore, it is amongst one of the important concepts of ML.
What is Supervised Machine Learning
Supervised learning is amongst the four types of machine learning in which we train the machines using well “labeled” training data. Based on that data, machines predict the output for the test data. The labeled data denotes some input data that we already tag with its corresponding correct output.
In supervised learning, the training data that we provide to the machines work as the supervisor for the training data that teach the machines to predict the output accurately. It tries to replicate the same learning method in which a student learns under the supervision of the teacher.
To put it in simple words, Supervised learning is a process of delivering input data as well as its corresponding correct output data to the machine learning model. The supervised learning algorithm aims to find a mapping function f(x) such that it maps the input variable(x) with the output variable(y).
How Does Supervised Machine Learning Work?
Unlike Unsupervised Learning, in supervised learning, we train the models using a labeled dataset, where the model learns about each type of pre-categorized data. Once the model completes the training process successfully, we test the model based on the test data, which we acquire as a subset of the training set, and then the model tries to predict the output.
To understand the working of Supervised Learning a little better, consider an example where we have a dataset of different types of shapes which we pre-label as square, rectangle, triangle, and Polygon. Now, for the first step, we need to train the model for the identification of each shape. We begin to train the data on the basis of the features of the variables like:
- If the given shape has four sides, and all of the sides are equal, then the model has to classify it as a Square.
- If the given shape has three sides, then the model has to classify it as a triangle.
- And, If the given shape has six equal sides, then the model will have to classify it as a hexagon.
Now, after successfully training the model with the labeled data, we test our model using the test dataset, and the task of the model is to identify the shape of the new entities that we provide as input.
As we have already trained the machine on all types of shapes, the model tends to classify the test shape on the basis of features like the number of sides and predicts the output, when it finds a new shape.
Steps Involved in Supervised Learning
1. The first step involves the determination of the type of training dataset
2. The next step is Collecting/Gathering the labeled training data.
3. After acquiring the data, we split the training dataset into three sections, namely, training dataset, test dataset, and validation dataset.
4. The model then tries to determine the input features of the training dataset, in an attempt to gain enough knowledge so that the model can accurately predict the output for the test data set.
5. After the model finds the useful features of the dataset, it tries to determine the suitable algorithm for the model, such as support vector machine, decision tree, and so on.
6. After the selection, the model tends to execute the algorithm on the training dataset. Sometimes we need to validate the sets as the control parameters, which are actually the subset of training datasets.
7. As the final step, we evaluate the accuracy of the prediction of the model by providing the test set. If the model predicts the correct output, implying that there is minimal variation between the expected and actual output, then we can interpret that the model is accurate.
Types of Supervised Learning
We can further divide Supervised Learning into two categories, namely, Regression and Classification. We will now learn about these types of Supervised Learning algorithms in brief.
1. Regression
Regression algorithms are a type of Supervised Learning that we use if there exists a relationship between the input variable and the output variable for the given dataset. We can use this algorithm for the prediction of continuous variables, in numerous fields such as Weather forecasting, Market Trends, and so on.
Some of the popular Regression algorithms which come under supervised learning are Linear Regression, Regression Trees, Non-Linear Regression, Bayesian Linear Regression, Polynomial Regression, and so on.
a. Linear Regression: We make use of Linear regression to identify the relationship between a dependent variable and one or more independent variables and is it even typically leverages in order to make predictions about future outcomes. When there exists a single independent variable and one dependent variable, we call it simple linear regression.
b. Naive Bayes: Naive Bayes is a classification approach that tends to adopt the principle of class conditional independence from the Bayes Theorem. This indicates that the presence of one feature will not impact the presence of another in the probability of a given outcome, and each predictor tends to have an equal effect on that result.
2. Classification
Classification algorithms are another type of Supervised Algorithm that we can use when the output variable is categorical, indicating that there are two classes for a particular input dataset such as Yes-No, Male-Female, True-false, and others like this.
Some of the popular algorithms that come under Supervised Learning are:
a. Random Forest: We can think of Random Forest as a huge collection of decision trees. Therefore, for a situation, there are many possible consequences that the random forest helps us to convey. It is not similar to a decision tree, as decision trees are always binary and they form a single unit, as it has multiple trees.
b. Decision Trees: Decision trees are binary trees, indicating that they are capable of having only two branches. This helps in the classification, which again is a type of supervised learning approach. It might have a higher complexity level depending on the number of leaves and nodes the tree consists of.
c. Logistic Regression: This type of regression assists us to understand the relationship between one binary dependent variable and another independent variable. This relationship, however, is not a linear relation. It is actually a logarithmic relation and we can represent it as
y = ln(P/(1-P)).
d. Support Vector Machines: SVM or Support Vector Machines are yet another very popular algorithms that we use in supervised learning.
They help us to classify and analyze the data by deciding a hyperplane in the given data set. The hyperplane is a decision boundary that is either a line or a plane, which divides the data points into two separate categories based on their similarities.
Examples of Supervised Learning Algorithms
- Linear Regression
- Nearest Neighbor
- Gaussian Naive Bayes
- Decision Trees
- Support Vector Machine (SVM)
- Random Forest
Advantages of Supervised Machine Learning
1. This type of learning is quite easy to understand. It is amongst the most common type of learning methods that we use for Machine Learning models.
2. We only need the training data for training the model. Due to its large size, it occupies a lot of space in the memory. However, we can remove it after the training is complete from the memory.
3. We will already have an idea of the number of classes in the data.
4. After training, the model can accurately judge for which specific data the output needs to be predicted as all the data in the collection is not important for the prediction of the desired output.
5. It proves beneficial for solving real-world computational problems.
6. This type of learning method can also help the model to learn from previous experiences. This in turn benefits the model to improve its accuracy in prediction.
Disadvantages of Supervised Learning
1. The performance of the model is limited to the fact that it cannot handle complex problems in the ML domain.
2. It cannot create labels of its own for the provided dataset. This indicates that it cannot discover data on its own as unsupervised learning does.
3. If we enter a new dataset in the model, it has to be from any of the predefined classes only.
4. It would require high-computational power with quality processors to train a supervised learning-based model. Thus, it is quite expensive and resource-consuming.
Applications of Supervised Learning
1. Risk Assessment
We make use of Supervised learning to analyze the risk in financial services or insurance domains in an attempt to minimize the risk portfolio of the companies.
2. Image Classification
Image classification is amongst one of the vital real-life applications of demonstrating the power of supervised machine learning. For example, consider how Facebook can recognize your friend in a picture from an album of tagged photos on the basis of Supervised Machine Learning.
3. Fraud Detection
We can extensively make use of Supervised Learning to identify whether the transactions made by the user are authentic or legitimate.
4. Visual Recognition
The ability of a Supervised machine learning model to identify objects, places, people, actions, and images finds itself beneficial for the visual recognition application.
5. Customer Sentiment Analysis
By Making use of the supervised machine learning algorithms, organizations are able to extract and classify important pieces of information from large volumes of data—including context, emotion, and intent—with very little human intervention. This use of a Supervised Algorithm makes it very handy for the industries to cater to consumer needs.
6. Spam Detection
Spam detection is another widely used example of a supervised learning model. By making use of supervised classification algorithms, organizations are able to train databases in order to recognize patterns or anomalies in new data to organize spam and non-spam-related correspondences effectively.
Use Cases of Supervised Learning
1. Security
One of the elementary concerns of today’s era is proper data security with the increase in data and related frauds. Above all of this, we have to be more conscious when it comes to the security of monetary credentials like a credit card, bank account number, and others.
With Supervised ML techniques, various tech giants and IT industries are now investing in novel and more powerful algorithms to tackle problems such as anomaly detection, fraud detection in credit cards, and many more. They are also useful as they create algorithms for spam filtering, blocking malicious links, emails, and so on.
2. Marketing and business
There is a wholesome amount of concern when it comes to marketing and business. We have to take special care, when the growth of the internet is exponential, as a consequence of which, the number of users gradually increases. This is a beneficial marketing opportunity for all companies globally to advertise their products with the rise of audience engagement through the internet.
We have major online retailers like Amazon, Walmart, Flipkart that are growing their business manifolds while keeping a high-profit margin.
Challenges Before Supervised Learning
1. Supervised learning models may tend to require certain levels of expertise to structure accurately.
2. Training supervised learning models may be very time-intensive.
3. Datasets ought to have a higher likelihood of human error, resulting in algorithms learning incorrectly.
4. Unlike unsupervised learning models, supervised learning is not able to cluster or classify data on its own.
Conclusion
With that, we have reached the end of this article that talked in brief about the basics of Supervised Machine Learning. Through this article, we realized that even though Supervised Learning does not have much real-life application, it still plays a pivotal role in the arena of Machine Learning. We came to know about the advantages, disadvantages, as well as applications of Supervised Learning.