Different Types of Machine Learning Algorithms
The different types of Machine Learning Algorithms are -
Supervised Machine Learning Algorithm
In this type of Machine Learning Algorithm, our model is trained on a labeled dataset.
The labeled dataset has both input & output parameters.
In this type of learning, both training & validation datasets are labeled.
Unsupervised Machine Learning Algorithm
In this type of Machine Learning Algorithm, the information is neither classified nor labeled.
Unsupervised Learning studies can infer a function to describe a hidden structure from unlabeled data.
This algorithm does not figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
Semi-supervised Machine Learning Algorithm
This algorithm falls between Supervised and Unsupervised Learning Algorithms because they use both labeled and unlabeled data for training.
This algorithm is used when labeled data requires skilled and relevant resources in order to train or learn from it.
The systems that use this method can improve learning accuracy to perform well.
Reinforcement Machine Learning Algorithm
This type of learning algorithm is a learning method that interacts with the environment by producing actions and discovers errors or rewards from the outcome.
Trial, error search and delayed rewards are the most relevant characteristics of reinforcement learning.
This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize performance.
Simple reward feedback is required for the agent to learn which action is best.