Why Nested Evaluation Matters
Why is it unfair to compare two tuned models on the same validation folds that were also used to pick their hyperparameters?
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中文题目Why is it unfair to compare two tuned models on the same validation folds that were also used to pick their hyperparameters?
打开 →A kernel SVM prediction uses two support vectors. Their signed contributions at a test point are +1.2 and -0.4, and the bias is -0.1. What score and predicted class result?
打开 →Using the degree-3 polynomial kernel K(x,z)=(x·z+1)^3, what is K((1,1),(2,-1))?
打开 →A desk only records post-launch performance for strategies that first clear an internal backtest hurdle. Why does regressing realized performance on backtest score inside the launched set generally fail to recover the unconditional relationship?
打开 →Why can fitting an unsupervised step like PCA or quantile normalization on all rows still make the final reported test error too optimistic?
打开 →equities · valuation · relative-valuation · multiples · comps · dcf · wacc · fcff
打开 →signal-evaluation · information-coefficient · rank-ic · spearman-ic · information-ratio · fundamental-law · grinold-kahn · breadth
打开 →fundamental-data · financial-statements · income-statement · balance-sheet · cash-flow-statement · valuation-ratios · p-e-ratio · ev-ebitda
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打开 →Two hyperparameter settings differ in mean CV score by only 0.001, while the estimated standard error is 0.010. What is the first sensible interpretation?
打开 →The current best setting sits at extreme values on both the learning-rate and regularization grids. What should your next search action be?
打开 →A categorical encoder was fit once on all rows and then reused inside cross-validation. What is the immediate correction?
打开 →Before you compare ROC and PR curves across models, what dataset property should you check first?
打开 →What is the first quantity you should pin down before optimizing a classification threshold?
打开 →A model looks poorly calibrated. What should you check first before concluding the model itself is broken?
打开 →A time-series tuning run says a very short lookback window wins, but recent live performance has deteriorated sharply. What should you inspect first before widening the search?
打开 →AUC improved a little after retraining. What should you ask first before declaring the new model practically better?
打开 →Training AUC is very high but CV AUC is near chance. Before trying more hyperparameter values, what is the first diagnostic step?
打开 →Why should you hesitate before selecting a perfectly calibrated model that has weak ranking power?
打开 →Why can short-dated CDS have a small RPV01 even when the spread itself is wide?
打开 →A strategist is building a scenario tree for inflation, GDP, and unemployment to assess portfolio vulnerability. Which measure is conceptually natural?
打开 →Model A makes 8 false positives and 2 false negatives on a validation set. Model B makes 6 false positives and 5 false negatives. If a false negative costs 10 units and a false positive costs 1 unit(s), which model has lower expected validation cost and what are the two costs?
打开 →A CDS market spread is 0.09 and RPV01 = 3.5. The desk observes an upfront of 0.14 per unit notional. Under upfront ≈ (s_mkt - coupon)*RPV01, what coupon is implied?
打开 →A CDS has market spread 0.04 and risky annuity RPV01 = 4.8. What standard coupon would make the linearized upfront equal 0.024 per unit notional?
打开 →The next payment of a growing perpetuity is 5 in one year and then grows at 0.02 forever. If the discount rate is 0.07, what is the present value?
打开 →The next payment of a growing perpetuity is 4.5 in one year and then grows at 0.025 forever. If the discount rate is 0.08, what is the present value?
打开 →周一上午,你在上海的一家 量化 私募。研究主管 在桌边停下来,看了一眼你 上周提交的 12 1 动量 信号的 DSL,问了一句话:「IC 是 多少?」这就是 4.2.3 模块 整个 评估 工序的 起点。你 已经 按 4.2.2 的 规范 把 信号 构造 完毕——alpha 公式 写好了,标准化 流水 跑通了,T+1 滞后 处理过了——下一步 不是 再 优化 ...
打开 →Under the flat-intensity approximation for a CDS, the par spread satisfies s ≈ lambda*(1-R). If the par spread is 0.018 and recovery is 0.4, what flat hazard lambda is implied?
打开 →A local-vol surface is parameterized by sigma_loc(S,t) = 0.2 + 0.12*(S/100 - 1). At what spot level S does the local volatility equal 0.212?
打开 →A local-vol surface is parameterized by sigma_loc(S,t) = 0.18 + -0.08*(S/80 - 1). At what spot level S does the local volatility equal 0.188?
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