Hybrid models are artificial intelligence models that combine different machine learning techniques to improve prediction performance and accuracy. These models combine features from different machine learning models and algorithms to take advantage of their strengths and minimise their weaknesses. Hybrid models are often used in machine learning applications that involve multiple data sources or multiple data types, such as in medical data analytics, financial data analytics and marketing data analytics. Examples of hybrid models include decision tree-based models and neural network-based models, which combine the simplicity and transparency of decision trees with the ability of neural network-based models to model complex relationships in data. Another example is the combination of rule-based models and deep learning-based models to take advantage of the ease of interpretation of rule-based models and the ability of deep learning-based models to model non-linear relationships in data.
Companies are increasingly aware of the importance of properly analyzing and managing the huge amount of data they store on a daily basis.
Read More »You now have everything you need to get down to work and start working with your company's data. After overcoming the first few hurdles of the [...]
Read More »OpenAI is a technology company created by the main leaders in artificial intelligence that, in its beginnings, defined itself as an organization that [...]
Read More »Before explaining what artificial intelligence is, we would like to start with a sentence from the book Age of intelligent machines (1992), by Raymond Ku [...]
Read More »
