In my journey as a data scientist, understanding the types of supervised learning has been pivotal. Supervised learning, as the cornerstone of machine learning, requires a nuanced grasp to truly leverage its potential in solving real-world problems. Throughout my career, I’ve encountered numerous scenarios where the right type of supervised learning technique made all the difference. From classification to regression, each type serves a unique purpose, tailored to specific datasets and objectives.
This insight is what I aim to share with you. Knowing the types of supervised learning not only broadens your toolkit but also deepens your problem-solving strategies. It’s essential for professionals aspiring to excel in the field to familiarize themselves with these types, understanding not just their theoretical underpinnings but also their practical applications. The journey through supervised learning is both challenging and rewarding, offering a rich landscape of methodologies to master.
Read more about the types of machine learning.
Let’s dive to look at different types of supervised learning.
What is Supervised Learning?
In Supervised Learning, a machine is trained using ‘labeled’ data. Datasets are said to be labeled when they contain both input and output parameters. In other words, the data has already been tagged with the correct answer.
So, the technique mimics a classroom environment where a student learns in the presence of a supervisor or teacher. On the other hand, unsupervised learning algorithms let the models discover information and learn on their own.
Supervised machine learning is immensely helpful in solving real-world computational problems. The algorithm predicts outcomes for unforeseen data by learning from labeled training data. Therefore, it takes highly-skilled data scientists to build and deploy such models. Over time, data scientists also use their technical expertise to rebuild the models to maintain the integrity of the insights given.
How Does it Work?
For instance, you want to train a machine in predicting your commute time between your office and home. First, you would create a labeled data set such as the weather, time of day, chosen route, etc. which would comprise your input data. And the output would be the estimated duration of your journey back home on a specific day.
Once you create a training set is based on corresponding factors, the machine would see the relationships between data points and use it to ascertain the amount of time it will take for you to drive back home. For example, a mobile application can tell you that your travel time will be longer when there’s heavy rainfall.
The machine may also see other connections in your labeled data, like the time you leave from work. You can reach home earlier if you start before the rush hour traffic hits the roads. Read more if you are curious to know about how unsupervised machine learning works.
Now, let us try to understand supervised learning with the help of another real-life example. Suppose you have a fruit basket, and you train the machine with all different kinds of fruits. Training data may include these scenarios:
- If the object is red in color, round in shape, and has a depression on its top, label it as ‘Apple’
- If the item has a greenish-yellow color and shaped like a curved cylinder, mark it as ‘Banana’
Next, you give a new object (test data) and ask the machine to identify whether it is a banana or an apple. It will learn from the training data and apply the knowledge to classify the fruit according to the inputted colours and shapes.
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Different Types of Supervised Learning
1. Regression
In regression, a single output value is produced using training data. This value is a probabilistic interpretation, which is ascertained after considering the strength of correlation among the input variables. For example, regression can help predict the price of a house based on its locality, size, etc.
In logistic regression, the output has discrete values based on a set of independent variables. This method can flounder when dealing with non-linear and multiple decision boundaries. Also, it is not flexible enough to capture complex relationships in datasets.
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2. Classification
It involves grouping the data into classes. If you are thinking of extending credit to a person, you can use classification to determine whether or not a person would be a loan defaulter. When the supervised learning algorithm labels input data into two distinct classes, it is called binary classification. Multiple classifications means categorizing data into more than two classes.
3. Naive Bayesian Model
The Bayesian model of classification is used for large finite datasets. It is a method of assigning class labels using a direct acyclic graph. The graph comprises one parent node and multiple children nodes. And each child node is assumed to be independent and separate from the parent.
As the model for supervised learning in ML helps construct the classifiers in a simple and straightforward way, it works great with very small data sets. This model draws on common data assumptions, such as each attribute is independent. Yet having such simplification, this algorithm can easily be implemented on complex problems.
Decision Trees
A decision tree is a flowchart-like model that contains conditional control statements, comprising decisions and their probable consequences. The output relates to the labelling of unforeseen data.
In the tree representation, the leaf nodes correspond to class labels, and the internal nodes represent the attributes. A decision tree can be used to solve problems with discrete attributes as well as boolean functions. Some of the notable decision tree algorithms are ID3 and CART.
4. Random Forest Model
The random forest model is an ensemble method. It operates by constructing a multitude of decision trees and outputs a classification of the individual trees. Suppose you want to predict which undergraduate students will perform well in GMAT – a test taken for admission into graduate management programs. A random forest model would accomplish the task, given the demographic and educational factors of a set of students who have previously taken the test.
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5. Neural Networks
This algorithm is designed to cluster raw input, recognize patterns, or interpret sensory data. Despite their multiple advantages, neural networks require significant computational resources. It can get complicated to fit a neural network when there are thousands of observations. It is also called the ‘black-box’ algorithm as interpreting the logic behind their predictions can be challenging.
Read: Top 10 Neural Network Architectures in 2020
6. Support Vector Machines
Support Vector Machine (SVM) is a supervised learning algorithm developed in the year 1990. It draws from the statistical learning theory developed by Vap Nick.
The algorithm of supervised learning in ML, SVM is highly popular amongst the supervised learning models as it can be used for classification or regression. Implementation of the model works well with high-dimensional spaces, but it can also be used effectively with small data sets. SVM can also classify new observations efficiently when the algorithm is trained on a data set. SVM performs this by creating singular or multiple hyperplanes to separate the data set between the two classes.
The approach of segregation that SVM has makes it unique and more efficient among all the supervised learning models. However, analysing data with high dimensions can become problematic for this model. The simple reason is that SVM increases the dimensionality of the given data set to segregate it properly. Like, in the case of solving linear problems, SVM adds a feature to the feature vector, called a classifier. This makes the two-dimensional data set, then, three-dimensional.
SVM separates hyperplanes, which makes it a discriminative classifier. The output is produced in the form of an optimal hyperplane that categorizes new examples. SVMs are closely connected to the kernel framework and used in diverse fields. Some examples include bioinformatics, pattern recognition, and multimedia information retrieval.
The primary goal of the Kernel function is to find a maximum-margin hyperplane that helps divide the observations into the maximum distance between the hyperplane and the nearest point from each class. Finding this constraint is important as the resulting hyperplane is less likely to result in overfitting. SVM uses different kernel functions such as similarity functions to turn a lower dimensional space into a higher one. The Kernel functions may change depending on the type of data set. SVM implements multiple Kernel functions the majority of the time as it is not immediately clear which kernel function is best suited to increase the dimensionality of the data.
Apart from these six supervised learning models, there is AdaBoost. Also known as Adaptive Boosting. This is a meta-algorithm that one can apply to the machine learning algorithm in order to enhance performance.
The working mechanism of AdaBoost creates a single strong classifier by combining multiple weak classifiers. The rationale behind doing so is the belief that weak classifiers are better than taking random classifications.
Adaboost is often labelled as an accumulated method as it works as a meta-algorithm instead of a collection of other supervised learning algorithms. This boosting technique relies on multiple independent classifiers and a weighting system that disseminates the data set to different classifiers and to weigh them as per their accuracy.
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Pros & Cons of Supervised Learning
Several types of supervised learning allow you to collect and produce data from previous experience. From optimizing performance criteria to dealing with real-world problems, supervised learning has emerged as a powerful tool in the AI field. It is also a more trustworthy method as compared to unsupervised learning, which can be computationally complex and less accurate in some instances.
However, supervised learning is not without its limitations. Concrete examples are required for training classifiers, and decision boundaries can be overtrained in the absence of the right examples. One may also encounter difficulty in classifying big data.
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Summing up
The long and short of supervised learning is that it uses labelled data to train a machine. The regression techniques and classification algorithms help develop predictive models that are highly reliable and have multiple applications.
Supervised learning requires experts to build, scale, and update models. In the absence of technical proficiency, brute-force may be applied to determine the input variables. And this could render inaccurate results. So, selection of relevant data features is essential for supervised learning to work effectively.
One should first decide which data is required for the training set, continue to structure the learned function and algorithm, and also assemble outcomes from experts and measurements. Such best practices can go a long way in supporting the accuracy of a model.
As artificial intelligence and machine learning pick up pace in today’s technology-oriented world, knowing about the types of supervised learning can be a significant differentiator in any field. The explanations above would help you take that first step!
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