# ds1000 / 928

- taskset: [ds1000](https://harnessreport.com/tasks/ds1000.md)
- difficulty: 
- category: 
- language: 
- runnable from the site: no
- agent timeout: 1800s

## Results by harness

_none yet_

## Instruction

```
# 928: DS-1000 Task

## Prompt
Problem:

I have set up a GridSearchCV and have a set of parameters, with I will find the best combination of parameters. My GridSearch consists of 12 candidate models total.

However, I am also interested in seeing the accuracy score of all of the 12, not just the best score, as I can clearly see by using the .best_score_ method. I am curious about opening up the black box that GridSearch sometimes feels like.

I see a scoring= argument to GridSearch, but I can't see any way to print out scores. Actually, I want the full results of GridSearchCV besides getting the score, in pandas dataframe.

Any advice is appreciated. Thanks in advance.


A:

<code>
import numpy as np
import pandas as pd
from sklearn.model_selection import GridSearchCV
GridSearch_fitted = load_data()
assert type(GridSearch_fitted) == sklearn.model_selection._search.GridSearchCV
</code>
full_results = ... # put solution in this variable
BEGIN SOLUTION
<code>

## What to do
- Edit `solution/solution.py` so the code passes the DS-1000 tests.
- Do not access the internet or install new packages; required libraries are preinstalled in the Docker image.
- Run tests locally via `bash tests/test.sh`.

## Notes
- Keep the variable names/signatures implied by the prompt/code_context.
- The evaluator uses the original DS-1000 `code_context` (`test_execution` / `test_string`).
```
---
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