ML Basic Terms

To be on the same page with AI-experts we need to build a special vocabulary with basic terms and concepts:
✏️ Feature - input parameter for the model. Usually it represents some characteristic of the entity or facts for which the model makes prediction.
✏️ Label - existing answer for input data. Usually used to train supervised models: predicted value can be compared with labels to check the size of discrepancy.
✏️ Loss - the difference between predicted value and label. For different models different functions to calculate loss is used.
✏️ Learning Rate - a floating-point number that tells the optimization algorithm the step size for the iteration while moving toward a minimum of a loss function. If the learning rate is too low, the model can take a long time to converge. If the learning rate is too high, the model may never converge.

#aibasics