Fastlad 【UPDATED - 2027】

Whether you are a classic car enthusiast looking for a 1:64 scale version of your dream ride or a scrapbooker wanting to shrink your memories, the Fastlad spirit is all about seeing the big picture in the smallest details. Paano Gawin ang Mini Magazine gamit ang Canva

| Aspect | Ordinary Least Squares (OLS) | Least‑Absolute‑Deviations (LAD) | |--------|------------------------------|---------------------------------| | | Minimize ∑ ( yᵢ − Xᵢβ )² | Minimize ∑ | yᵢ − Xᵢβ | | | Loss function | Quadratic (smooth, differentiable) | Linear (non‑smooth at 0) | | Sensitivity to outliers | High (outliers pull the fit) | Low – each outlier contributes linearly | | Statistical interpretation | MLE under Gaussian errors | MLE under Laplace (double‑exponential) errors | | Closed‑form solution | Yes (β = (XᵀX)⁻¹Xᵀy) | No – requires linear programming / iterative methods | fastlad

# Simulated data with outliers np.random.seed(0) n, p = 200_000, 20 X = np.random.randn(n, p) beta_true = np.random.randn(p) y = X @ beta_true + np.random.laplace(scale=0.5, size=n) Whether you are a classic car enthusiast looking

# Fit FastLAD (ADMM, parallelized) model = LADRegressor(max_iter=1000, rho=1.0, verbose=True) model.fit(X, y) p = 200_000