Supervised Learning vs Unsupervised Learning (Pros and Cons)
Supervised Learning vs Unsupervised Learning (Pros and Cons). In this article, we will elucidate the main differences between Supervised Learning and Unsupervised Learning and weigh their pros and cons.
In today’s cloud driven, smart digital era, machine learning has made its way through all industries and niches, right from the educational, healthcare, and finance sector, to government entities as well.
To cater to the needs of the consumer, businesses are leveraging AI and ML technologies to offer top class experiences. We see variety of examples of ML algorithms around us such as face recognition in phones, fraud detection alerts, visual recognition, and many more.
Now, Machine Learning takes either of the two fundamental approaches to analyze datasets. Supervised Learning and Unsupervised Learning.
So, shall we start with Supervised Learning vs Unsupervised Learning (Pros and Cons).
What is Supervised Learning?
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As the name suggests, Supervised Learning is an ML approach that uses labelled datasets for analysis. These datasets are designed in a way to “supervise” or train algorithms to accurately classify data and predict outcomes.
Meaning, in supervised learning, some data is already tagged with the right answer. The model precisely uses labelled inputs and outputs for analysis and learning over time.
Supervised Learning is further classified into two kinds of methods when data mining:
1. Classification
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Classification is a supervised learning technique that makes use of an algorithm to assign test data accurately to a particular category. This method is used for email spam detection, fraud detection, diagnostics, image classification, medical imaging, etc.
For this, classification problems leverage various algorithms such as:
- Naive Bayes classifiers.
- Linear classifiers.
- Decision trees.
- Random forest.
- Support Vector Machines (SVM).
- K-NN (k-nearest neighbor).
- Neural Networks.
- Logistic Regression.
2. Regression
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The regression method uses an algorithm to learn and understand the relationship between independent and dependent variables. This is useful for numerical prediction by considering various metrics, such as risk assessment, ROI projections in business, score predictions, housing price prediction, etc.
Some of the most common regression algorithms are:
Pros of Supervised Machine Learning
The major advantages of supervised machine learning are shown below:
- Clusters data and generates data outputs based on previous references and experiences.
- Optimizes performance criteria based on experiences.
- Helps resolve real world computation problems seamlessly.
- You know the number of classes in the training data accurately.
- Simpler process to understand than unsupervised learning.
- You can train the classifier with a particular definition of the classes and decision boundary for better accuracy.
Cons of Supervised Learning
The major disadvantages of supervised machine learning are shown below:
- Classifying big data can be overwhelming.
- Cannot handle very complex tasks in machine learning.
- Data training period is longer, since supervised learning requires a lot of computation time.
- Is not able to provide unknown information from the training data.
- Cannot cluster or classify data on its own by discovering hidden features .
- The output will be wrong, if the input provided does not belong to any of the classes within training data.
What is Unsupervised Learning?
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Unsupervised Learning makes use of ML algorithms to discover, analyse and cluster unlabelled or unclassified data sets. These algorithms execute data mining and discover hidden patterns without any “supervision” or human intervention, hence the name unsupervised learning.
Here the machine has to group unsorted data based on its patterns, differences, or similarities without any prior training data provided to the machine.
Unsupervised learning models are commonly used for clustering, association, and dimensionality reduction:
1. Clustering
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Clustering is a technique used in data mining to group unlabeled data together as per their differences or similarities. K-means clustering algorithms use the ‘K value’ to indicate granularity and size of grouping, as per which it assigns similar data points into clusters.
Such a technique is commonly used for targeted marketing, biology, customer segmentation, city planning, image compression, and more.
Common algorithms used for clustering are:
- K-means.
- KNN (k-nearest neighbor).
- PCA.
- SVD (Single Value Decomposition).
- Principal Component Analysis.
- Independent Component Analysis.
2. Association
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Association is a technique that uses different rules to discover the relationship between parameters in a dataset. This unsupervised learning technique is commonly used for recommendation engines and affinity analysis. For example, recommendations in eStores such as “Frequently Bought Together”, “You May Also Like This” or “Customers Also Liked This”.
3. Dimensionality reduction and Denoising
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Dimensionality Reduction is another technique used whenever the “dimensions” in a dataset are too high or there are redundant features. It aims to retain data integrity whilst reducing the data inputs to a manageable size, so it takes less storage space and less computation time.
Often such technique is used in a stage of pre processing data, for example in autoencoders, face recognition, image recognition, big data visualization, text mining, and so on.
Pros of Unsupervised Machine Learning
The major advantages of Unsupervised Machine Learning are shown below:
- Learns and classifies data on its own without any labels, hence saving a lot of manual work.
- Once the data is classified, you can add labels.
- Makes it easier to find hidden patterns in the data, which isn’t easy otherwise and extremely helpful in real world applications.
- A perfect tool for data scientists to understand raw data.
- Helps to discover to what degree there are similarities within data sets with the help of probabilistic techniques.
- Learns slowly and then computes outcome; this makes it similar to human intelligence.
Cons of Unsupervised Machine Learning
The major disadvantages of Unsupervised Machine Learning are shown below:
We cannot define accurate information for data sorting and outcome in an unsupervised task. It heavily relies on the machine and the model.
- Users need to manually interpret and label the classes linked to that classification.
- Results may be less accurate.
- Can be costly as it requires human guidance to learn the patterns and correlate them to base knowledge.
- Sometimes results can be useless, as there is no output measure or label to confirm its benefits.
Welcome to the main part of this article blog is Supervised Learning vs Unsupervised Learning. Now it is time to learn about the differences.
Supervised vs Unsupervised Machine Learning
| Parameters | Supervised Machine Learning | Unsupervised Machine Learning |
|---|---|---|
|
Process |
In this model, input and output data is labelled for analysis and outcome prediction. |
In this model, only input data is given. |
|
Input data |
Algorithms are trained using labelled data. |
Algorithms are used to analyse and cluster unlabelled data. |
|
Computing complexity |
Simpler than unsupervised learning. |
Computing is complex. |
|
Use of data |
Uses training data to discover the relationship between input and output variables. |
Does not use output data. |
|
Accuracy |
More accurate. |
Less accurate. |
|
Feedback |
Has feedback mechanism to check whether predictions are correct or not. |
Does not have feedback mechanism. |
|
Data analysis |
Offline analysis |
Real time data analysis. |
|
Number of classes. |
Known |
Unknown |
|
Types of algorithms used. |
SVM, Linear Regression, Logistics, Regression, Random forest, Decision trees, Neural Networks. |
Hierarchical clustering, SVD, K-means, KNN, etc. |
|
Application |
Used for prediction. |
Used for analysis. |
|
AI |
Does not resemble Artificial Intelligence, as the model is trained for each dataset. |
It resembles Artificial Intelligence as the ML model continues to learn new patterns and insights over time. |
Key Differences Between Supervised and Unsupervised Learning
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1. Labelled Data
The main difference between Supervised Learning vs Unsupervised Learning is using labelled datasets.
One one hand, supervised learning uses labelled data for input and output, whereas unsupervised learning does not. Algorithms in supervised learning will “learn” using training data and iteratively make predictions to match the correct answer. For this, the labelled data has to be accurate; one mistake in labelling the data may result in a wrong answer.
On the other, unsupervised learning algorithms will discover hidden patterns in unlabelled data on their own. Users need to simply validate the output variables so as to confirm whether the outcome is useful or not.
2. Objectives
Consequently, with supervised learning, it aims to make outcome predictions for new data and you are likely to know the expected results.
Oppositely, in unsupervised learning, the main objective is to derive insights from large sets of data. ML algorithms discover what is useful or interesting from the large volumes of datasets.
3. Use Cases
Equally, with use cases in supervised learning, it is used for spam detection, fraud detection, weather forecasting, pricing predictions, sentiment analysis, medical imaging, image classification, and so on.
Use cases with unsupervised learning is used for anomaly detection, market segmentation, customer personas, recommendation engines, autoencoders, image recognition, face recognition, and more.
4. Complexity
Complexity with supervised learning is a comparatively simpler technique for ML which commonly uses R or Python for computing.
Evidently, with unsupervised learning is a computationally complex model. that requires powerful tools and large training set to derive expected outcomes.
5. Downsides
Finally, supervised learning takes a lot of time for training data, besides labelling input and output variables also need expertise and precision.
But the unsupervised learning is likely to produce less accurate outcomes, without human intervention to validate the output variable.
Thank you for reading Supervised Learning vs Unsupervised Learning (Pros and Cons). We should now conclude.
Supervised Learning vs Unsupervised Learning (Pros and Cons) Conclusion
Summarizing, choosing a learning technique between Supervised Learning and Unsupervised Learning heavily relies upon the use cases, and how the data scientists assess the data volume and structure.
Here are some factors to keep in mind when picking an ideal ML approach:
- Evaluate your input data to see whether it is unlabelled or labelled. Do you have the required expertise for additional data labelling with accuracy?
- Define the goal to understand what objectives you want to achieve or what problem you need to solve with ML. Or, do you want the algorithm to predict new issues? Decide which ML technique you want to leverage- classification, regression or clustering, or association.
- Decide which algorithms to use. As stated above, there are many algorithms commonly used for both types of learning. Check whether the chosen algorithm has the required features and attributes you need and whether it supports your given dataset, its volume, and its structure. Is the volume of the dataset too large? Are you looking for highly accurate outcomes? Choose likewise.
Out of both, which strategy to use for machine learning? The answer is it completely depends upon your main goal and the type of algorithm you use.
Each learning algorithm has its own purpose and they perform uniquely as per the type of operation. You need to choose an ideal algorithm that suits your use case.
