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226. Ensemble averaging
easy
Generalsenior
Implement ensemble averaging to combine predictions from multiple ML models into one final prediction. You’ll take a list of per-model predictions and return the averaged prediction, which is a simple but common baseline for improving stability.
Ensemble averaging is defined as:
Requirements
Implement the function
python
Rules:
- Compute the element-wise mean across models (average over the first dimension).
- Return a NumPy array of floats with the same length as a single model’s prediction list.
- Do not use any prebuilt ensembling utilities (e.g., from scikit-learn).
- Use NumPy for the numeric computation.
- Keep it as a single Python function.
Example
python
Output:
python
Input Signature
| Argument | Type |
|---|---|
| predictions | np.ndarray |
Output Signature
| Return Name | Type |
|---|---|
| value | np.ndarray |
Constraints
Use NumPy; no sklearn ensembling utilities.
Average across models dimension (axis=0).
Return np.ndarray[float].
Hint 1
Ensure predictions is a 2D NumPy array of float.
Hint 2
Use np.mean(..., axis=0) to average across models (rows) for each item (column).
Hint 3
Return the result as a NumPy array; ensure output length equals one model’s list.
Roles
ML Engineer
AI Engineer
Companies
GeneralLevels
senior
entry
Tags
numpy-mean
ensemble-averaging
vectorization
aggregation
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Python EditorLn 1, Col 1