In our previous article (here), we discussed the classification of the Machine Learning models on five different bases. Based on the nature of the input data we provide to the machine learning algorithms, ML models can be classified into four major categories.

• Supervised Learning
• Unsupervised Learning
• Semi-Supervised Learning
• Reinforcement Learning

In this article, we will discuss these categories in more detail.

Key questions that are being answered in this blog

• What are Supervised, unsupervised, semi-supervised, and Reinforcement Learning? How are they related to each other? 
• Why are these terms named “Supervised” and “Unsupervised”?
• How are classification, regression, or clustering algorithms linked with supervised and unsupervised Learning?
• What are the famous algorithms associated with supervised or unsupervised approaches? Why are most real-life scenarios more likely to be related to semi-supervised Learning?

Let’s start and dive deeper into these categories without any further delay.

Supervised Learning

Supervised Learning is a category in which we feed labeled data as input to the machine learning model.

The input and output values are already known, and the machine learning algorithm learns the mapping function. Mathematically, for Y as the Output and X as the input, machine learning algorithms try to find the best mapping function f such that Y = f(X). If you observe closely, Learning happens like some supervisor supervises the learning process. We already know the answers; hence algorithms try to map the function so that the predicted output must be close to the actual output. 

Let’s say the machine has learned a mapping function f predicting the values Y’ for every X passed as an input to the function. Once the difference between predicted (Y’) and actual (Y) goes below a certain threshold (in simple terms, errors become negligible), learning stops.

Supervised Learning can be further categorized as follows:

1. Classification: Taking the example of the below image, images of shapes are the input to the machine learning model, and labels of those images as shape names (The labeling process is called annotation) is the output data. Based on these input and output data, the model learns to predict the category of unseen image data, whether it is a rectangle, circle, triangle, or hexagon.

2. Regression: The example of the below image shows the experience (in years) on the X-axis. For every experience, there is one salary (per month Rupees) on the Y-axis. Green dots are the coordinates (X, Y) in the form of Input and Output data. The regression problem tries to find the continuous mapping function from input to output variables, for example, the blue line in the image.

If the order of the mapping function is fixed to 1, which is a linear function, the model will learn the blue line shown in the image.

Some famous use cases of Supervised Learning are:

• Object detection and Image Classification: Find whether a cat is present in the image or not; if yes, then find the location of the cat in that image.
• Recommendation systems: If someone has bought a new home, automatically suggest new furniture as most people bought them together.
• Time Series prediction: If the last three days’ atmosphere temperature of India was 21°C, 22°C, and 21°C, respectively, then what would be the temperature for tomorrow?

Some Frequently used algorithms in Supervised Learning

• Linear Regression & Logistic Regression
• SVM (Support Vector Machines)
• Random Forest

Unsupervised Learning

Unsupervised Learning is a category of machine learning in which we only have the input data to feed to the model but no corresponding output data.

Here, we know the value of input data, but the output and the mapping function are both unknown. In such scenarios, machine learning algorithms map the function that finds similarity among different input data instances (samples) and group them based on the similarity index, which is the output for unsupervised Learning. So we can say that algorithms generate a pseudo output for learning the mapping function.

In such Learning, there is no supervision as there is no existence of output data. Hence they are called Unsupervised Learning. Algorithms try to find the similarity between different input data instances by themselves using a defined similarity index. One of the similarity indexes can be the distance between two data samples to sense whether they are close or far.

Unsupervised Learning can further be categorized as:

1. Clustering (Unsupervised classification): Using the example below, we have input data consisting of images of different shapes. Machine learning algorithms try to find the similarity among other images based on the color pixel values, size, and shapes and form the groups as outputs in which similar input instances lie. If you notice, squares get clustered together, and similarly, the circles and hexagons.

Clustering Algorithms are:

• Hierarchical clustering
• K-means clustering

2. Dimensionality Reduction: When the attributes of the data samples have more than three dimensions, there is no way to visualize the relationship among the attributes, as we can not plot variables in more than 3 Dimensions. But without analyzing the input data, we can never be sure about the Machine Learning model’s performance. To solve this purpose, we use dimensionality reduction techniques to bring down the total number of dimensions and analyze the data. Suppose we want to study 10 features together, but there is no way to visualize the 10D plots. So, we try to reduce the dimensions to 3 or lower to easily plot it, analyze their relationship and do further processing.

Dimensionality Reduction Algorithms are:

• PCA (Principal Component Analysis)
• t-SNE

3. Association: Such Learning is more about discovering rules that describe a large portion of the data. Customers who bought a banana also bought carrots, or Customers who bought a new house also bought new furniture. At first, it will look similar to clustering, but clustering is about finding the relationship among data points, and association is about finding the relationship among attributes/features of those data points. 

Some famous use cases of Unsupervised Learning are:

1. Market Segmentation: Whether the market is hot or cold is based on the money revolving in the market.
2. Fraud detection: Categorize the transactions into fraudulent and non-fraudulent groups.
3. Image Segmentation: Gray color objects in an image taken from a car represents roads.

Some Frequently used algorithms in Unsupervised Learning :

1. K-means
2. Apriori Algorithm for learning association rule.
3. Principal Component Analysis

Semi-Supervised Learning

Semi-supervised Learning is a category of machine learning in which we have input data, and only some input data are labeled. In more technical terms, we can say the data is partially annotated.

Semi-supervised Learning is partially supervised and partially unsupervised.

Let’s take one example from the below image to make it clear. Suppose a bucket consists of three fruits, apple, banana, and orange. Someone captured the image of all three but labeled only the orange and banana images. Here, the machine first will classify the new apple image as not a banana and not orange. Then someone will observe these predictions and label them as apples. Then retraining the model with that label will give it the ability to classify apple images as an apple.

Nowadays, capturing a tremendous amount of data has become a trend. Many big companies have collected millions of Terrabytes of data and are still collecting. But labeling the collected data requires workforce and resources; hence, it’s too expensive. And this is the main reason that many real-life databases fall into this category.

Some famous use cases of Semi-supervised Learning are:

In such type of Learning, one can use either.

• Supervised Learning: Train a model on the labeled data and then use that Learning to predict the output for unlabeled data. This output for the corresponding unlabeled input data can now be treated as the pseudo label. Now we can use partial and pseudo-labeled data to train another supervised learning model.

For example, suppose there is a large chunk of data in the image above, and a small amount of labeled dataset is present. We can train the model using that small amount of labeled data and then predict on the unlabelled dataset. Prediction on an unlabelled dataset will attach the label with every data sample with little accuracy, termed a Pseudo-labeled dataset. Now we can train a new model with a mixture of the true-labeled and pseudo-labeled datasets.

Or

• Unsupervised Learning: We can capture and learn the structure present in the data.

Reinforcement Learning

Here, Machine Learning algorithms act as virtual agents in the known environment where these agents choose the possible options of action. The agent selects the best action from all the possibilities present in that environmental state and, based on that selection, receives reward/risks. The algorithms keep an eye on maximizing the reward, reducing the risk, and eventually learning.

Let’s take one example. Suppose we want our computer to learn “How to balance the pole on the moving cart”? We might have played this game in childhood, balancing sticks on our palms.

Here, the hand is replaced by a cart. Suppose we want to make our cart smart enough to balance the stick. So “cart” is the agent, the plane on which the cart will move is the environment, and the cart is taking possible actions such as moving either left or right to balance the stick. So, whenever a stick falls in either direction, the cart will take appropriate action to make it stand. Now suppose we said to our agent that the longer the time it holds the stick upright, the higher the reward will be.

After every action, the state of the cart and the stick in the environment will change. Our agent will analyze that state and again take different actions best suitable for that particular state.

Some famous use cases of Reinforcement Learning are:

If we look at the history of machine learning, we will find that RL is quite old and has been in the industry for a more extended period. But because of the requirement of awareness of the entire environment states, it is usually used with simulated environments. Some of the most common use cases where it is being used in the industry are:

• The agent can drive the vehicle inside the simulated environment.
• Predicting the stock price in the stock market.
• Agents that can play games.

Some Frequently used algorithms in Reinforcement Learning :

• Q-Learning
• Deep Q-Learning (DQN)

Critical Questions to explore

• Why do we name different machine learning algorithms as supervised or unsupervised Learning?
• Why is semi-supervised learning the most common case in Machine Learning?
• How can we utilize semi-supervised Learning in case of object detection problems?
• Can we think of why supervised Learning can never bring the future we expect from Machine Learning?
• Why is the annotation process required?

Conclusion

In this article, we described machine learning classification based on the “Nature of input data.” We came across the definition of Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning and talked about some industry use-case or real-life use-case of these categories. We also listed some famous algorithms associated with each category. We hope you have enjoyed the article.

Quick Note

Next Blog: Classification and Regression Problems in Machine Learning

Previous Blog: Classification of ML Models

Enjoy Learning, Enjoy Algorithms!