Interpolation When to Use Use this skill when working on interpolation problems in numerical methods. Decision Tree 1. Assess Data Characteristics - How many data points? Spacing uniform or non-uniform? - Is data smooth or noisy? - Need derivatives at endpoints? 2. Select Interpolation Method - Few points (<10): Polynomial (Lagrange, Newton) - Many points, smooth data: Cubic splines - Noisy data: Smoothing splines or least squares - High dimensions: Use simplex-based (n+1 neighbors vs 2^n) 3. Implement with SciPy - - natural cubic spline - - B-spline - - 1D interpolation 4. Validate Results -…