Machine Learning 편집하기 (부분)

==Key Concepts in Machine Learning==
Several foundational concepts are essential to understanding machine learning:
*'''Model''': A mathematical representation of patterns learned from data, used to make predictions or decisions.
*'''Training and Testing''': The process of training involves using a dataset to teach the model patterns. Testing evaluates the model’s performance on new, unseen data.
*'''Features and Labels''': Features are input variables, while labels are the target output. Models learn to predict labels based on features.
*'''Overfitting and Underfitting''': Overfitting occurs when a model learns noise instead of patterns; underfitting happens when the model is too simple to capture data trends.
*'''Bias-Variance Tradeoff''': The balance between a model’s complexity and its generalization ability. High bias models are often too simple, while high variance models are overly complex.