Feedback refers to information that is provided to a machine learning system or model after it has made a prediction or decision. Feedback is used to improve model performance by correcting errors and updating model parameters accordingly.
Feedback can be positive or negative, and can be provided in a supervised or unsupervised manner. In supervised learning, feedback is provided in the form of training labels that are used to adjust the model. In unsupervised learning, feedback is provided through comparison of model predictions with real-world observations.
Feedback is important in machine learning because it allows models to adapt and improve over time. Without feedback, models may stagnate at sub-optimal solutions and not be able to learn effectively from new data. In addition, feedback is also important for the evaluation of model performance, as it allows the comparison of model predictions with actual observations and the identification of possible errors or inconsistencies.
How is artificial intelligence helping us? Artificial intelligence (AI) has gone from being the stuff of science fiction movies to a [...]
Read More »Unlike a computer program, in which a list of commands are processed through a computer program, AI goes beyond the [...]
Read More »The Official Chamber of Commerce of Seville, in collaboration with the Spanish Institute of Financial Analysts (IEAF), offered last March 16th [...]
Read More »Artificial intelligence (AI) and machine learning (ML) are two of the most popular technologies used to build intelligent systems for the [...]
Read More »
