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BigCodeBench / BigCodeBench v0.1.0_hf 4b2c1b2e-2728-5ada-96fb-72786c3ae12e

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.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import numpy as np

def task_func(data):
    ''' 
    Train a simple linear regression model based on the given data and evaluate the model by calculating the mean square error. The data should be structured with 'Hours' as independent variables and 'Scores' as dependent variables.
    The function set the random set when dividing the train and test data to 42 and the test set size is 0.2

    Parameters:
    - data (dict): The dictionary with keys 'Hours' and 'Scores', representing study hours and respective scores.

    Returns:
    float: The mean squared error between the actual scores and predicted scores based on the test split.

    Requirements:
    - pandas
    - sklearn.model_selection.train_test_split
    - sklearn.linear_model.LinearRegression
    - numpy

    Example:
    >>> task_func({'Hours': [10, 20, 40], 'Scores': [90, 80, 70]})
    25.0
    '''

instruct prompt

Train a simple linear regression model based on the given data and evaluate the model by calculating the mean square error. The data should be structured with 'Hours' as independent variables and 'Scores' as dependent variables. The function set the random set when dividing the train and test data to 42 and the test set size is 0.2
The function should output with:
    float: The mean squared error between the actual scores and predicted scores based on the test split.
You should write self-contained code starting with:

Code

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import numpy as np
def task_func(data):

code prompt

Code

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import numpy as np
def task_func(data):

entry point

task_func

libs

  • pandas
  • numpy
  • sklearn

Discussion

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Source and history

task_id
4b2c1b2e-2728-5ada-96fb-72786c3ae12e
task_key
default--v0~2e1~2e0~5fhf--4b2c1b2e-2728-5ada-96fb-72786c3ae12e
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import