Why is feedback loop important in machine learning?
Also, feedback loop is important when the predictions of a model affect the future labels, as the machine learning model is solving the problem the labeling logic might get changed as the predictions and action around that changed the past behaviors. In these cases also feedback loop help in updating the label and retraining the model.
How to get better at machine learning in real life?
In particular, I will contrast the workflow Kaggle reinforced with the typical development workflow of a real-life machine learning project. Throughout the post, I will give coloring examples around topics such as problem definition, feature engineering, model debugging, productionization, and feedback loops.
Why is labeling data important in machine learning?
In real life application supervised machine learning depends on labeled datasets and quality of data labels have huge effect on the model performance. It is important to label the data as per the ground truth.
How is reinforcement learning used in machine learning?
Currently a straight forward approach on my mind is, first we start with a initial set of instances and train a model with them. Then each time the model makes a wrong prediction, we add the wrong instance into the training set. This is different from blindly enlarge the training set because it is more targeting.
How can we optimize feedback loops in ML?
Given a set of input features, return the probability of a lead conversion. Given the probability of a lead conversion, call the client back. If we have a giant amount of leads, and only a small number of callbacks, how can we optimize this?
Which is an example of a dangerous feedback loop?
This is an example of a real world feedback loop. This feedback loop is dangerous for a company, but can be even more dangerous for a society. In the example above, the feedback loop means that we are dropping Facebook and Bing leads because of historical conversion rates.