What are the main differences between reinforcement learning and other ML techniques like supervised or unsupervised learning?
|
next → ← prev Show
Difference between Supervised and Unsupervised LearningSupervised and Unsupervised learning are the two techniques of machine learning. But both the techniques are used in different scenarios and with different datasets. Below the explanation of both learning methods along with their difference table is given.  Supervised Machine Learning:Supervised learning is a machine learning method in which models are trained using labeled data. In supervised learning, models need to find the mapping function to map the input variable (X) with the output variable (Y). Supervised learning needs supervision to train the model, which is similar to as a student learns things in the presence of a teacher. Supervised learning can be used for two types of problems: Classification and Regression. Learn more Supervised Machine Learning Example: Suppose we have an image of different types of fruits. The task of our supervised learning model is to identify the fruits and classify them accordingly. So to identify the image in supervised learning, we will give the input data as well as output for that, which means we will train the model by the shape, size, color, and taste of each fruit. Once the training is completed, we will test the model by giving the new set of fruit. The model will identify the fruit and predict the output using a suitable algorithm. Unsupervised learning is another machine learning method in which patterns inferred from the unlabeled input data. The goal of unsupervised learning is to find the structure and patterns from the input data. Unsupervised learning does not need any supervision. Instead, it finds patterns from the data by its own. Learn more Unsupervised Machine Learning Unsupervised learning can be used for two types of problems: Clustering and Association. Example: To understand the unsupervised learning, we will use the example given above. So unlike supervised learning, here we will not provide any supervision to the model. We will just provide the input dataset to the model and allow the model to find the patterns from the data. With the help of a suitable algorithm, the model will train itself and divide the fruits into different groups according to the most similar features between them. The main differences between Supervised and Unsupervised learning are given below:
Note: The supervised and unsupervised learning both are the machine learning methods, and selection of any of these learning depends on the factors related to the structure and volume of your dataset and the use cases of the problem.Next TopicRegression Analysis in Machine learning ← prev next → What is the difference between Reinforcement Learning and machine learning?Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, an artificial intelligence faces a game-like situation.
What makes Reinforcement Learning different than supervised learning?Reinforcement learning differs from supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task.
What is the difference between supervised ml and unsupervised ML?Supervised learning model predicts the output. Unsupervised learning model finds the hidden patterns in data. In supervised learning, input data is provided to the model along with the output. In unsupervised learning, only input data is provided to the model.
What is machine learning describe supervised unsupervised and Reinforcement Learning with suitable example?Supervised and Unsupervised learning. |
