One key difference between machine learning and traditional statistical approaches lies

in the size of the data. In this context, size primarily refers to the number of features $($or independent

variables$),$ leading to what is known as a high$-$dimensional data set. Incorporating more features into

a model, regardless of its type, increases the model$$s flexibility and complexity. However, this addition

of features inevitably leads to a trade$-$off. The greater complexity of these models raises concerns about

overfitting. Can you explain, in simple terms, what this trade$-$off entails? Additionally, please provide a

non$-$technical defense of the machine learning approach despite this issue