Example of Interactive learning Interactive learning is a type of machine learning that involves a feedback loop between the machine learning model and a human or another agent that provides feedback. Here’s an example of how interactive learning might work:
Let’s say you want to create a machine learning model that can identify different types of flowers. You start by training your model on a dataset of labeled flower images. However, you notice that the model is struggling to distinguish between two types of flowers that look very similar.
To improve the model’s accuracy, you decide to use interactive learning. You show the model several images of the two similar-looking flowers and ask a human expert to label them correctly. The model then tries to classify new images on its own, and if it gets an image wrong, it sends the image to the human expert for feedback.
The expert provides the correct label, and the model updates its parameters to improve its classification accuracy. This process continues, with the model becoming better at classifying the flowers over time as it receives more feedback from the human expert. Eventually, the model may become accurate enough to classify the flowers with high accuracy without human intervention.
This is just one example of how interactive learning can be used to improve the accuracy of a machine learning model. It can be applied in various domains, such as natural language processing, computer vision, and robotics, among others.