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BigCodeBench / BigCodeBench v0.1.0_hf 12e79bb8-974b-5170-89c5-3a535573de1e

Problem

Answer published by the source. Consult the official source to check your work against its answer.

complete prompt

import pandas as pd
from sklearn.linear_model import LinearRegression

def task_func(df, target):
    """
    Perform a linear regression analysis on a given DataFrame.
    
    Parameters:
    - df (pd.DataFrame): The pandas DataFrame.
    - target (str): The target variable.
    
    Returns:
    - score (float): The R-squared score of the model.

    Requirements:
    - pandas
    - sklearn

    Example:
    >>> import numpy as np
    >>> np.random.seed(42)
    >>> df = pd.DataFrame({'feature': np.random.rand(100), 'target': np.random.rand(100)})  # Explicitly using pd
    >>> r_squared = task_func(df, 'target')
    >>> print(r_squared)
    0.0011582111228732872
    """

instruct prompt

Perform a linear regression analysis on a given DataFrame.
The function should output with:
    score (float): The R-squared score of the model.
You should write self-contained code starting with:

Code

import pandas as pd
from sklearn.linear_model import LinearRegression
def task_func(df, target):

code prompt

Code

import pandas as pd
from sklearn.linear_model import LinearRegression
def task_func(df, target):

entry point

task_func

libs

  • pandas
  • sklearn

Discussion

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Source and history

task_id
12e79bb8-974b-5170-89c5-3a535573de1e
task_key
default--v0~2e1~2e0~5fhf--12e79bb8-974b-5170-89c5-3a535573de1e
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import