benchmarks.wiki

benchmarks.wiki / Public workspace

BigCodeBench / BigCodeBench v0.1.0_hf 372aa797-186a-56ed-9f9a-211de1cd3994

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
from sklearn.model_selection import train_test_split


def task_func(csv_file_path, attribute, test_size=0.2, random_state=42):
    """
    Train a linear regression model on a dataset and predict the value of a particular attribute.
    This function reads a CSV file to create a pandas DataFrame, separates the data into 
    training and testing sets, and performs linear regression. It returns the predicted 
    values for the testing set as well as the trained model.

    Parameters:
    csv_file_path (str): The path to the CSV file containing the data set.
    attribute (str): The attribute to predict.
    test_size (float, optional): Proportion of the dataset to include in the test split. Default is 0.2.
    random_state (int, optional): Seed used by the random number generator. Default is 42.

    Returns:
    tuple: A tuple containing:
        - model (LinearRegression): The trained linear regression model.
        - predictions (ndarray): An array of predicted values for the test set.

    Requirements:
    - pandas
    - sklearn.linear_model
    - sklearn.model_selection

    Note: The function assumes that the CSV file is correctly formatted and that the specified attribute exists.

    Example:
    >>> model, predictions = task_func("/path/to/data.csv", "target")
    >>> print(predictions)
    [123.45, ..., 126.78]
    """

instruct prompt

Train a linear regression model on a dataset and predict the value of a particular attribute. This function reads a CSV file to create a pandas DataFrame, separates the data into training and testing sets, and performs linear regression. It returns the predicted values for the testing set as well as the trained model.
Note that: The function assumes that the CSV file is correctly formatted and that the specified attribute exists.
The function should output with:
    tuple: A tuple containing:
    model (LinearRegression): The trained linear regression model.
    predictions (ndarray): An array of predicted values for the test set.
You should write self-contained code starting with:

Code

import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
def task_func(csv_file_path, attribute, test_size=0.2, random_state=42):

code prompt

Code

import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
def task_func(csv_file_path, attribute, test_size=0.2, random_state=42):

entry point

task_func

libs

  • pandas
  • sklearn

Discussion

Discussion

No discussion posts on this page yet. Share a minimal failing example, an algorithm with its complexity, or a reproducible command and result. Use the posting template.

See answer Answer published by the source

Artifacts

Code, notes and reproducible work shared by participants. Files are served from a separate origin.

No artifacts on this page yet. Share reproducible code or notes in a contribution. Share a minimal failing example, an algorithm with its complexity, or a reproducible command and result. Use the posting template.

Source and history

task_id
372aa797-186a-56ed-9f9a-211de1cd3994
task_key
default--v0~2e1~2e0~5fhf--372aa797-186a-56ed-9f9a-211de1cd3994
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import