Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to enable AI to use human intelligence to learn and program. The concept of machine learning is pretty vast where Supervised and unsupervised machine learning are the two main types of machine learning which is extensively used in today’s era. In Supervised learning the data is fed in a machine that is capable of working on a set of labeled datasets. Whereas, Unsupervised learning deals with unlabeled data.
Now, without wasting much of the time, let’s begin with the article and explore supervised and unsupervised machine learning in detail. Getting insights into their types, advantages, disadvantages, applications and much more.
Supervised And Unsupervised Machine Learning – Key Takeaways
- Understanding the concept of Supervised and unsupervised machine learning with the help of Examples.
- Getting familiar with the types of Supervised and unsupervised machine learning and common algorithms used in each type.
- Getting Insights into the applications of supervised and unsupervised machine learning.
- Understanding the evaluation process of supervised and unsupervised machine learning.
What is Supervised learning?
Supervised Machine learning justifying its name, involves a supervisor as a teacher. This process involves teaching the machine to work with a set of data that is well-labeled, Which means some data is already tagged with the correct answer. Later, the machine is fed with a new set of data so that the learning algorithm analyses the training data and produces an appropriate outcome from labeled data.
Example:
Suppose we have a fruit basket that you want to identify. The machine and the programmed algorithm would first analyze the image to verify and record various visual features such as its shape, color, and texture. Then, it would draw a comparison between these features and the already learned features of the fruits ( dataset) and then it would make predictions for the new dataset. If the new image’s features are most similar to those of an apple, the machine would predict that the fruit is an apple.
Types of Supervised Learning
Supervised learning is further classified into two more algorithms:
- Regression: A regression problem is when the output variable is an individual data element and has a real value, such as “height” or “weight”.
- Classification: A classification problem deals with output variables that are more of a category, such as “bike” or “car”, “fruits” or “vegetables”.
1. Regression Supervised Machine Learning
Regression learning is a technique that deals with individual data elements and thus predicts an output variable based on one or more labeled input variables. This type of supervised learning first establishes a relationship between the dependent variable and the independent variables. Some common regression learning algorithms include-
- Polynomial Regression
- Ridge Regression
- Lasso Regression
- Decision Tree Regression
- Random Forest Regression
- Support Vector Regression (SVR)
2. Classification Supervised Machine learning
Classification learning is a technique that predicts a categorical output variable based on the fed input variables rather than a single individual output. This involves categorization of data into predefined groups. Unlike the Regression Supervised learning technique where a relationship is established between input and output only, in Classification algorithms a special function is used to map the input to a probability distribution over various output classes. Some common classification algorithms include:
- Logistic Regression
- Support Vector Machines
- Decision Trees
- Random Forests
- Naive Baye
Evaluating Supervised Learning Models
Evaluating the supervised learning models are certain important steps undertaken to ensure that the model is accurate and can work on generalized inputs. It is very essential to ensure they perform well on unseen data as well, thus these models come into play for the same purpose. There are a variety of different metrics that can be used to increase the efficiency of supervised learning models, some of them include:
For Regression
- Mean Squared Error (MSE): MSE evaluates the average squared difference between the predicted output and the actual labelled values.
- Root Mean Squared Error (RMSE): RMSE is simply the square root of MSE, which represents the standard deviation of the prediction errors.
- Mean Absolute Error (MAE): MAE evaluates the average absolute difference between the predicted output values and the actual labelled data values.
- R-squared: Also known as the Coefficient of Determination , R-squared evaluates the proportion of the variance in the target variables mentioned in the model.
For Classification
- Accuracy: Accuracy is defined as percentage of predictions (outputs) that the model makes correctly.
- Precision: Precision is defined as the percentage of positive predictions that the model makes that are actually true.
- Recall: Recall is defined as the percentage of all positive examples that the model correctly identifies.
- F1 score: The F1 score is defined as the weighted average of precision and recall which are mentioned in the above types.
- Confusion matrix: A confusion matrix is defined as a table that shows the number of predictions for each group or class, along with the actual class labels.
Applications Of Supervised Learning
Supervised and unsupervised machine learning is used to solve a variety of problems in today’s modern world, let us look towards some of the common applications of supervised machine learning:
- E-commerce Recommendation Systems: Supervised learning helps in recommending similar products to users based on their browsing history and preferences. Therefore, this helps the user to get desired results on their main browser webpage.
- Retail Inventory Management and sales: These supervised learning algorithms can predict future demand for products which further helps in optimizing inventory levels. Moreover, it can estimate future sales based on the history of market trends
- Healthcare and Medical services: Supervised learning techniques can also predict diseases based on the information of the patient which is already stored in the machine. It uses the symptoms or medical history of a patient to carry out such analysis.
- Fraud detection: Supervised learning models are intelligent enough to analyze financial transactions and identify their patterns to check for any fraudulent activity. This is extensively used in various financial institutions to prevent fraud and protect the authenticity of their servers and their customers.
Advantages Of Supervised Learning
- Supervised learning performs with a clear objective and allows to collect data and produces output from previous experiences.
- Supervised learning algorithms are very accurate and efficient. These models can achieve high accuracy and performance by making precise and careful predictions based on the labelled data set fed into the machines. This helps to optimize performance and user experience.
- Supervised machine learning includes algorithm that are very flexible in nature. These can accommodate various types of real-world computation problems and thus have a wide Range of Applications with diverse algorithms working on specific problems.
- Supervised learning models can also automate various time-consuming tasks, such as sorting emails, diagnosing medical images, and detecting fraudulent transactions.
- Supervised learning algorithms are designed in a way that they can handle large datasets very efficiently. They can make predictions based on the history of machines and thus are very scalable in the long run.
Disadvantages Of Supervised Learning
- Certain supervised learning algorithms might not work efficiently when it comes to the Classification of big data. This can further lead to complexities in its computation and limited scalability.
- Supervised learning requires a large amount of labeled data i.e. we need to feed a lot of data into the machines before the entire process gets automated. Therefore, it can be quite time-consuming and expensive in many cases.
- Training for supervised learning needs a lot of computation time. This includes training human resources for the initial stages of this process. Certain supervised learning algorithms can be complex to understand thus it requires a lot of time.
What is Unsupervised learning?
Unsupervised Machine learning deals with unlabeled data. These algorithms are designed to discover hidden patterns or data groupings without any human intervention. This type of learning discovers similarities and differences in information and makes relevant output. It is the training of a machine using data that is not labeled thus allowing the algorithm to predict without any prior guidance. Here the machine works in grouping information according to similarities and observed patterns.
Example:
For instance, you have a model with a large dataset of unlabeled images, containing both apples and bananas. The model has not been given any prior information about the features of these images. Unsupervised machine learning comes into play here to identify the patterns of the data elements. The machine then therefore can categorize all the elements into two categories of apples and bananas based on the pattern of similarities and differences observed by the machine itself without any prior information.
Types of Unsupervised Learning
Unsupervised learning is classified into two categories of algorithms:
- Clustering: It is a technique in unsupervised machine learning used to group and form clusters of similar data points together based on their inherent characteristics.
- Association: Association is a technique of unsupervised machine learning that attempts finding relationships or patterns among a set of items fed into a machine.
1. Clustering Unsupervised Machine Learning
Clustering in Unsupervised Learning is a technique used to group and form clusters of similar data points together based on their inherent characteristics. Briefly explained we make groups of similar data points together. This algorithm works by moving similar datasets closer to their cluster server or centers simultaneously, thus increasing the distance between non-similar data elements and clusters. Clustering algorithm has various types like:
- Hierarchical clustering
- K-means clustering
- Principal Component Analysis
- Singular Value Decomposition
- Independent Component Analysis
- Gaussian Mixture Models (GMMs)
- Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
2. Association Unsupervised Machine learning
Association is a technique of unsupervised machine learning that attempts finding relationships or patterns among a set of items fed into a machine. We use algorithms to detect patterns in the data. Rules are made describe large portions of your data, such as people who buy X also tend to buy Y.
Eg : if a person bought milk, he/she would also buy cereals. Some common association rule learning algorithms include:
- Apriori Algorithm
- Eclat Algorithm
- FP-Growth Algorithm
Evaluating Non-Supervised Learning Models
Evaluating non-supervised learning models are certain important steps undertaken to ensure that the model is accurate and can work on generalized inputs. It is very essential to ensure they perform well on unseen data and this is where these models come into play. There are a variety of different metrics that can be used to increase the efficiency of these algorithms , these includes:
- Silhouette score: The silhouette score is defined as the measure of closeness of similar clusters. It is calculated on the grounds of how well each data point is clustered with its own cluster members. It falls in the range of -1 to 1.
- Calinski-Harabasz score: The Calinski-Harabasz score is defined as the ratio between the variance between clusters and the variance within clusters. This falls in the range of 0 to infinity.
- Adjusted Rand index: The adjusted Rand index is defined as the extent of similarities between two clustering’s. It falls in the range of -1 to 1.
- Davies-Bouldin index: The Davies-Bouldin index is defined as the average similarities between clusters. This falls in the ranges of 0 to infinity.
However, It can be a bit difficult as we don’t have data labeled in advance to make further predictions even through these models and metrics.
Application Of Unsupervised Machine Learning
As we have discussed above, supervised and unsupervised machine learning can be used to solve a wide variety of problems, the common applications of unsupervised learning include:
- Anomaly and Fraud Detection: Identifying unusual patterns in transaction and and identify their patterns to check for any fraudulent activity. This is extensively used in various financial institutions to prevent fraud and protect the authenticity of their servers and their customers. This also works in ensuring network security by detecting unusual network traffic by frauds and illegitimate users which conspire a security breaches or attack.
- Customer Behavior Analysis : These algorithms are designed in a way that these can identify patterns in the nature a particular customer shops . Unsupervised machine learning algorithms can analyze customer likes and dislikes by feedback and reviews lodged by the consumer itself and thus help in improving their experience. This even allows businesses to fulfill their targets and improve services more effectively.
- Scientific discovery and development: Unsupervised machine learning models are intelligent enough to detect and discover hidden relationships and patterns in scientific data. By establishing relationships between these facts and figures these algorithms can predict hypotheses and new scientific technologies in various fields.
- Healthcare and Medical services: Unsupervised machine learning models can also predict diseases based on the information of the patient which is already stored in the machine . It uses the symptoms or medical history of a patient to carry out such analysis.
- Recommendation systems: Unsupervised machine learning algorithms work in identify patterns and similarities in the behavior of the user and thus understand its preferences to recommend products, movies, or music according to their interests.
Advantages of Unsupervised Learning
- Unlike supervised learning, the unsupervised machine learning algorithms does not require training data to be labeled. This decreases maximum human intervention as no prior information has to be entered in the system.
- Unsupervised learning algorithms are capable of finding unknown patterns in data this can further help you to gain insights from unlabeled data. By studying the patterns of data, predictions can be made and thus new theories and hypothesis can also be made using these algorithms.
- Unsupervised machine learning models are very adaptive in learning new trends and patterns of unknown data without human intervention, this therefore increases automation of the machines and thus also helps in achieving high scalability when working with large sections of data.
- Unsupervised machine learning models are cost efficient as they do not require labeled data. This significantly reduces the cost of human resources required for the same and effort associated with data collection and labeling it as per the requirements of the machine.
Disadvantages of Unsupervised Learning
- Unsupervised machine learning algorithms can face difficulties to measure accuracy as they lack predefined predictions and output. Thus the results often have lesser accuracy.
- A lot of time is required by the user to interpret and understand the complex relationships and patterns of the data. Thus, we can say that these algorithms are not time efficient.
- Unsupervised learning can be sensitive to data quality, which means that these algorithms are generally sensitive to noise in the data.
- High-dimensional data is used in these algorithms which can be a challenge as these are difficult to interpret and finding meaningful patterns in this dataset can be a big task in itself.
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Supervised And Unsupervised Machine Learning FAQs
What is the difference between both the machine learning techniques?
Supervised learning basically uses labeled data with the goal of mapping data from inputs to outputs and its basic types include- Regression, Classification etc. Unsupervised Learning on the other hand uses unlabeled data to identify patterns, structures, or groupings in data and it’s basic types include: Clustering, Association, etc.
What are the other types of machine learning algorithms?
Besides supervised and unsupervised machine learning algorithms, we have several other types including: Semi-Supervised Learning, Reinforcement Learning, Self-Supervised Learning, Transfer Learning, Multitask Learning, Active Learning etc.
What is overfitting in supervised learning?
Overfitting occurs when a model learns too much noise from the training data, fitting the training data too closely and performing poorly on new, unseen data.
