题目826 · 脑筋急转弯
7 identical jobs each require 3 minutes on Stage 1 and then 5 minutes on Stage 2. Each stage handles at most one job at a time, and a job can enter Stage 2 immediately after Stage 1 if Stage 2 is free. What is the earliest completion time of all jobs?
打开 →题目4178 · 机器学习
A raw daily return of 4.8% is winsorized to the range [-3%, 3%], then standardized using trailing mean 0.5% and trailing standard deviation 1.0%. What z-score feature results?
打开 →题目4333 · 机器学习
Why should you hesitate before ruling out RNNs entirely in a trading-system pipeline?
打开 →题目2449 · 机器学习
For one issuer, the three training rows sum to 12. A pipeline mistakenly demeans by the full-sample issuer mean 3.6 computed from five rows total. What is the sum of the two held-out rows for that issuer?
打开 →题目1720 · 统计
Suppose only 1% of tested trading ideas are genuinely predictive. A testing pipeline has 80% power and a 5% false-positive rate. Conditional on obtaining a positive result, what fraction of positives are truly real?
打开 →题目2464 · 机器学习
Someone argues there was no leakage because the code never accessed test labels. Give the core reason this defense can fail in real ML pipelines.
打开 →题目4315 · 机器学习
In an overparameterized network, why is it a mistake to discuss regularization strength without also looking at optimizer and data pipeline choices?
打开 →题目4182 · 机器学习
A pipeline standardizes each feature using the mean and standard deviation of the full dataset before creating the train/test split. Is that clean?
打开 →题目2466 · 机器学习
You are auditing a pipeline for leakage. Beyond checking the split line in the final dataframe, what is the highest-value thing to inspect in the code path?
打开 →题目4264 · 机器学习
When can PCA actually hurt a predictive pipeline?
打开 →题目2519 · 机器学习
Why is tuning lambda on the test set just as problematic here as in any other ML pipeline?
打开 →题目2426 · 机器学习
Why might a practitioner prefer Huber or pseudo-Huber loss when the data pipeline occasionally produces corrupted labels or sensor spikes?
打开 →题目2453 · 机器学习
A pipeline clips a spread feature at the 1st and 99th percentiles computed on the full panel before the split. Why can this still bias the reported test score even though the clipping rule is unsupervised?
打开 →