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4204Negative Linear FormIn an SVM dual solution, one training point has α i=0.4 with C=1.0. What does that suggest about the point's role relative to the margin?机器学习中等derivation未尝试面试订阅4206Higher C in a Noisy DatasetAn RBF kernel uses distance squared 2. If γ doubles from 0.5 to 1.0, by what factor does the kernel similarity change?机器学习中等derivation未尝试面试订阅4207Smaller C and Margin ToleranceA degree-2 polynomial kernel is K=(x·z+1) 2. If x·z increases from 1.0 to 1.5, by what percentage does K increase?机器学习中等derivation未尝试面试订阅4208Larger Gamma in an RBF KernelA soft-margin SVM keeps ||w|| 2 and total hinge loss fixed, but C increases from 0.5 to 1.5 while total hinge loss is 2.0. How much does the objective increase?机器学习中等derivation未尝试面试订阅4209Smaller Gamma in an RBF KernelAt a test point, one support vector contributes +0.9. If its dual coefficient is halved and everything else stays fixed, what contribution remains from that support vector?机器学习中等derivation未尝试面试订阅4210When Kernels Beat Manual Feature LiftsA linear SVM score at a point is 1.2 before feature rescaling. If the relevant feature values are all doubled while w is held fixed, what new score contribution comes from that linear term?机器学习中等derivation未尝试面试订阅4211Why Only Support Vectors Matter at Prediction TimeIf your features are extremely sparse one-hot indicators with huge dimension, would you usually try a linear SVM or an RBF SVM first, and why?机器学习中等essay未尝试面试订阅4212Why the Dual Is Attractive in Kernel SVMsWhy is feature scaling especially important before using an RBF SVM?机器学习中等essay未尝试面试订阅4213Why Hard Margin Is FragileWhy do only support vectors matter at prediction time in an SVM?机器学习中等essay未尝试面试订阅4214Why Kernels Are Not FreeWhy can choosing C extremely large increase overfitting risk in a soft-margin SVM?机器学习中等essay未尝试面试订阅4215A Fast Sanity Check for Kernel-SVM AnswersWhy can a high-degree polynomial kernel become numerically and statistically awkward on unscaled features?机器学习中等essay未尝试面试订阅4226High-Cardinality ID TrapA random forest says a hashed customer ID is the most important feature by impurity decrease, even though the validation permutation drop is almost zero. What is the most likely trap?机器学习中等derivation未尝试面试订阅4227Leakage Proxy TrapA model ranks a 'days since settlement' field as highly important in a fraud predictor, but that field is only known after the case outcome becomes visible. What is wrong with reading that as genuine predictive importance?机器学习中等derivation未尝试面试订阅4228Proxy Feature TrapA tree model gives most importance to ZIP code instead of the underlying income and region variables. Why should you be cautious before concluding ZIP code is the true driver?机器学习中等derivation未尝试面试订阅4229Correlation Split Credit TrapTwo nearly identical features alternate as top splitters across different random seeds. Does that mean the signal is unstable?机器学习中等derivation未尝试面试订阅4230Negative Permutation ImportanceA weak feature shows slightly negative permutation importance on a finite validation set. Should you immediately conclude it is genuinely anti-predictive?机器学习中等derivation未尝试面试订阅4236Importance Is Not CausalityWhy is it dangerous to treat feature importance as if it were a causal ranking?机器学习中等essay未尝试面试订阅4237Why Trees Overcredit Splittable FeaturesWhy do impurity-based importances tend to overcredit features with many possible split points?机器学习中等essay未尝试面试订阅4238Why Correlation Makes Rankings FragileWhy do strongly correlated features make importance rankings fragile?机器学习中等essay未尝试面试订阅4239Why You Need Multiple Importance ViewsWhy is it often wise to look at more than one feature-importance diagnostic?机器学习中等essay未尝试面试订阅