Differentiate between Supervised Learning and Unsupervised Learning. (Mention 4 points in each category)
Answers
Answer:
Explanation:
Process
In a supervised learning model, input and output variables will be given.
In unsupervised learning model, only input data will be given
Input Data
Algorithms are trained using labeled data.
Algorithms are used against data which is not labeled
Algorithms Used
Support vector machine, Neural network, Linear and logistics regression, random forest, and Classification trees.
Unsupervised algorithms can be divided into different categories: like Cluster algorithms, K-means, Hierarchical clustering, etc.
Computational Complexity
Supervised learning is a simpler method.
Unsupervised learning is computationally complex
Answer:
What is Supervised Machine Learning?
In Supervised learning, you train the machine using data which is well "labeled." It means some data is already tagged with the correct answer. It can be compared to learning which takes place in the presence of a supervisor or a teacher.
A supervised learning algorithm learns from labeled training data, helps you to predict outcomes for unforeseen data. Successfully building, scaling, and deploying accurate supervised machine learning Data science model takes time and technical expertise from a team of highly skilled data scientists. Moreover, Data scientist must rebuild models to make sure the insights given remains true until its data changes.What is Unsupervised Learning?
Unsupervised learning is a machine learning technique, where you do not need to supervise the model. Instead, you need to allow the model to work on its own to discover information. It mainly deals with the unlabelled data.
Unsupervised learning algorithms allow you to perform more complex processing tasks compared to supervised learning. Although, unsupervised learning can be more unpredictable compared with other natural learning deep learning and reinforcement learning methods.
Why Supervised Learning?
Supervised learning allows you to collect data or produce a data output from the previous experience.
Helps you to optimize performance criteria using experience
Supervised machine learning helps you to solve various types of real-world computation problems.
Why Unsupervised Learning?
Here, are prime reasons for using Unsupervised Learning:
Unsupervised machine learning finds all kind of unknown patterns in data.
Unsupervised methods help you to find features which can be useful for categorization.
It is taken place in real time, so all the input data to be analyzed and labeled in the presence of learners.
It is easier to get unlabeled data from a computer than labeled data, which needs manual intervention