The last blog provided information on what is supervised and unsupervised learning. This space will spread some information on the difference between them and what could be a midway solution for it.
Supervised learning VS. unsupervised learning
The significant difference between the two approaches is the use of labeled datasets. In most simplified terms, supervised learning uses labeled input and output data, while an unsupervised learning algorithm does not require any data.
The algorithm “learns” from the training dataset by iteratively predicting the data and adjusting for the correct response in supervised learning. Although supervised learning models are more reliable than unsupervised learning models, they necessitate human involvement at the output to appropriately label the data.
For example, a supervised learning model can predict how long it will take to prepare a dish based on the ingredients and instruments needed. But to do so, it is necessary to train the machine about the materials used and the food preparation method.
On the other side, unsupervised learning models work independently to find the inherent structure of the unlabeled data. Here, there are chances that it still requires some human intervention for validating output variables.
For example, an unsupervised learning model can successfully identify online shoppers who often purchase groups of products simultaneously. Here, a data analyst plays a role in validating a recommendation engine to group baby clothes with diapers, applesauce, and sippy cups.
How to choose between supervised vs. unsupervised learning?
Working according to the right approach depends completely on the situation. Also, a data scientist is responsible for choosing the right path based on the structure and volume of data and the use case. Before settling on which method to use, one must be sure of the following –
- Checking on input data – Cross-checking whether the entered data is labeled or unlabeled while catching up with the experts who can support additional labeling.
- Setting goals – Need to identify whether the problem defined is a well-defined or recuring one or is it the algorithm that will predict new problems.
- Considering multiple choices for the algorithm – Considering only those algorithms that can actually fulfill the conditions and can support data volume and structure.
It isn’t easy to differentiate big data in supervised learning, but the results obtained are way accurate and trustworthy. While talking about unsupervised learning, it can take charge of a large chunk of data in real-time. Additionally, unsupervised learning doesn’t show up transparency for how the data is grouped, and so there might be a high chance of inaccurate results.
Semi-supervised learning: A solution
Semi-supervised learning is a midway solution to use supervised or unsupervised learning. Semi-supervised learning is a perfect medium where a training dataset with both labeled and unlabeled data is applied.
Semi-supervised learning can be used when it becomes difficult to extract relevant features from data and also when there is a high volume of data.
Semi-supervised learning may turn helpful for medical images, where a small amount of training data can lead to a significant improvement inaccuracy. A radiologist, for example, can mark a small subset of CT scans for tumors or diseases so that the computer can better predict which patients need additional medical attention.
Key factors distinguishing supervised and unsupervised learning
For supervised learning, the goal involves predicting outcomes for new data. The type of results to expect are also very upfront. While for the unsupervised learning algorithm, the goal is to gain insights from large volumes of new data. The machine learning itself withdraws results on what use is derivable from the dataset.
Supervised learning models prove ideal for spam detection, sentiment analysis, weather forecasting, and pricing predictions, among other things. At the same time, unsupervised learning is a great fit for anomaly detection, recommendation engines, customer personas, and medical imaging.
While counting on the complexity, supervised learning becomes a simple method for machine learning that uses programs such as R or Python to complete the process. In supervised learning, powerful tools are used to deal with a large amount of unclassified data. Unsupervised learning models are complicated as they require an extensive training set to produce intended outcomes.
Supervised learning models often require more time to train the data and the labels for input and output variables under the supervision of experts. On the other hand, unsupervised learning methods can give out wildly inaccurate results unless there is a human intervention to validate the output variables.
A marked difference is recorded between supervised and unsupervised learning. It is for sure that both techniques will require to make much more significant strides in the machine learning field. Both the techniques will evolve and develop, marking more innovations in the ML and AI fields.
Deciding on which technique to use typically depends on the structure and volume of data and the use case. In many situations, data scientists prefer supervised learning and unsupervised learning approaches together to solve the use cases.
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