Then, they make predictions and provide information about the performance of these learning algorithms as output. Meta learning algorithms use metadata of learning algorithms as input. Meta learning helps researchers understand which algorithm(s) generate the best/better predictions from datasets. It is used to improve the results and performance of a learning algorithm by changing some aspects of the learning algorithm based on experiment results. Meta learning, also known as “learning to learn”, is a subset of machine learning in computer science. In computer science, meta learning studies and approaches started in the 1980s and became popular after Jürgen Schmidhuber and Yoshua Bengio‘s works on the topic. Meta learning algorithms can learn to use the best predictions from machine-learning algorithms to make better predictions.
Meta learning algorithms make predictions by taking the outputs and metadata of machine-learning algorithms as input. Meta learning can be used for different machine learning models (e.g., few-shot learning, reinforcement learning, natural language processing, etc.). This results in better predictions in a shorter time. Meta-learning approaches help find these and optimize the number of experiments.
Many experiments are required to find the best-performing algorithm and parameters of the algorithm. The performance of a learning model depends on its training dataset, the algorithm, and the parameters of the algorithm.
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