General
PromptBeginner5 minmarkdown
| Numerical
symmetric dist | Mean imputation |
0
Explore
Find agent skills by outcome
50,584 skills indexed with the new KISS metadata standard.
Showing 24 of 50,584Categories: Data & Insights, General, Data
General
PromptBeginner5 minmarkdown
| Numerical
time-series | Forward fill / Interpolation |
0
General
PromptBeginner5 minmarkdown
| Numerical
skewed dist | Median imputation |
0
General
PromptBeginner5 minmarkdown
→ Check for disguised missing values (?
N/A
0
General
PromptBeginner5 minmarkdown
- Any disguised nulls found (?
N/A
0
General
PromptBeginner5 minmarkdown
[Column] — Reason: [e.g.
72% missing
0
General
PromptBeginner5 minmarkdown
assert remaining_test.sum() == 0
fTest still has missing:\n{remaining_test[remaining_test > 0]}
0
General
PromptBeginner5 minmarkdown
After completing Phases 1–4
deliver this exact report:
0
General
PromptBeginner5 minmarkdown
assert remaining_train.sum() == 0
fTrain still has missing:\n{remaining_train[remaining_train > 0]}
0
General
PromptBeginner5 minmarkdown
X_test = X_test.drop(columns=drop_cols
errors='ignore')
0
General
PromptBeginner5 minmarkdown
# imp_iter = IterativeImputer(max_iter=10
random_state=42)
0
General
PromptBeginner5 minmarkdown
# STEP 12 — MICE / IterativeImputer (most powerful
use when needed)
0
General
PromptBeginner5 minmarkdown
X_train
X_test
0
General
PromptBeginner5 minmarkdown
X_train = X_train.drop(columns=drop_cols
errors='ignore')
0
General
PromptBeginner5 minmarkdown
X
y
0
General
PromptBeginner5 minmarkdown
df.dropna(subset=[TARGET_COL]
axis=0
0
General
PromptBeginner5 minmarkdown
df.replace(DISGUISED_NULLS
np.nan
0
General
PromptBeginner5 minmarkdown
DISGUISED_NULLS = [?
N/A
0
General
PromptBeginner5 minmarkdown
│ Linear Models (LogReg
LinearReg
0
General
PromptBeginner5 minmarkdown
│ SVM
KNN Classifier: │
0
General
PromptBeginner5 minmarkdown
│ → Use max_iter=10
random_state=42 for reproducibility │
0
General
PromptBeginner5 minmarkdown
│ Tree-based (XGBoost
LightGBM
0
General
PromptBeginner5 minmarkdown
│ → Creates: col_was_missing = 1 if NaN
else 0 │
0
General
PromptBeginner5 minmarkdown
→ df.dropna(subset=[TARGET_COL]
inplace=True)
0