advantage and disadvantages for newton forward and backward interpolation formula
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Pros:
Higher-order polynomials can exactly fit larger datasets (by construction).
They are simpler to evaluate than non-polynomial approximations.
Cons:
Because of their rigidity (due to smoothness), they tend to over-fit the data.
This over-fitting is a serious issue, which is why it is often much better to use a spline, i.e., a collection of polynomials stitched together
Higher-order polynomials can exactly fit larger datasets (by construction).
They are simpler to evaluate than non-polynomial approximations.
Cons:
Because of their rigidity (due to smoothness), they tend to over-fit the data.
This over-fitting is a serious issue, which is why it is often much better to use a spline, i.e., a collection of polynomials stitched together
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