[QUESTION] Chapter 1: How reducing the number of features of training data effects overfitting?

[rupeshchiluka001]

In second edition of this book, In page no 28, While discussing about overfitting,

In the text book, it is given as:

Simplify the model by selecting one with fewer parameters (e.g., a linear model rather than a high-degree polynomial model), by reducing the number of attributes in the training data, or by constraining the model.

In the above paragraph, I didn't understand how reducing the number of attributes in the training data will simply the model? and how this controls the overfitting?

Thanks in advance :)

[rupeshchiluka001]

I misunderstood the sentence. Its actually by reducing the attributes of the data we can reduce the parameters of the model and thus simplifying the model.