models of machine learning.

How Does Machine Learning Work?

 


According to UC Berkeley  the learning system of a machine learning algorithm can be divided into three main components:

 

1. Decision Process: Machine learning algorithms are designed to make predictions or classifications. Given some input data, which can be either labeled or unlabeled, the algorithm generates an estimate based on patterns it identifies in the data.

 

2. Error Function: This function evaluates the model's predictions. If there are known examples, the error function compares the model's output to these examples to determine the accuracy of the predictions.

 

3. Model Optimization Process: To improve the model's fit to the training data, weights are adjusted to minimize the difference between the actual examples and the model's predictions. The algorithm continuously performs this iterative "evaluate and optimize" process, autonomously updating weights until it achieves a desired level of accuracy.

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