Label-prediction length mismatch
Review this Python scoring code.
Accuracy is mediocre and inconsistent even though the model is good. Find the alignment bug.
What a strong answer looks like
Separate real bugs from style. Rank issues by severity, point at the root cause rather than the symptom, and suggest a concrete fix, specific and kind.
0:00 of about 20 min
Mark a line and say what kind of problem it is.0 findings
1import numpy as np
2import pandas as pd
3from sklearn.metrics import accuracy_score
4
5def score(model, df):
6 # df has features + a 'label' column; some rows were dropped upstream
7 feats = df.drop(columns=["label"])
8 feats = feats.dropna() # drop rows with missing features
9 preds = model.predict(feats.values) # numpy array, length = len(feats)
10 labels = df["label"].values # length = len(df)
11 return accuracy_score(labels[: len(preds)], preds)
Which questions mattered is sealed until you submit. Telling you now would just be handing over the edge cases.
Run or narrate your approach, then ask the coach.