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BigCodeBench / BigCodeBench v0.1.0_hf 71898bf0-60b2-51b0-b603-aa17c3634319

Problem

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

complete prompt

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


def task_func(n_samples=100, n_features=10, random_seed=None):
    """
    Generate synthetic data using a simple regression model, fit a linear regression model to the data,
    and return the predicted values along with the coefficients and intercept of the model.

    Parameters:
    - n_samples (int): The number of samples for the synthetic data. Default is 100.
    - n_features (int): The number of features for the synthetic data. Default is 10.
    - random_seed (int, optional): The seed for reproducibility. Default is None.

    Returns:
    - tuple: A tuple containing:
        - predictions (numpy.ndarray): The predicted values of the test set.
        - coefficients (numpy.ndarray): Coefficients of the linear regression model.
        - intercept (float): Intercept of the linear regression model.
        - mse (float): Mean squared error of the model predictions.

    Requirements:
    - numpy
    - sklearn.datasets.make_regression
    - sklearn.model_selection.train_test_split
    - sklearn.linear_model.LinearRegression
    
    Example:
    >>> predictions, coefficients, intercept, mse = task_func(100, 5, random_seed=42)
    >>> predictions[:3]
    array([ 180.79207843, -295.0210232 ,  118.23799221])
    >>> round(mse, 4)
    0.0113
    """

instruct prompt

Generate synthetic data using a simple regression model, fit a linear regression model to the data, and return the predicted values along with the coefficients and intercept of the model.
The function should output with:
    tuple: A tuple containing:
    predictions (numpy.ndarray): The predicted values of the test set.
    coefficients (numpy.ndarray): Coefficients of the linear regression model.
    intercept (float): Intercept of the linear regression model.
    mse (float): Mean squared error of the model predictions.
You should write self-contained code starting with:

Code

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
def task_func(n_samples=100, n_features=10, random_seed=None):

code prompt

Code

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
def task_func(n_samples=100, n_features=10, random_seed=None):

entry point

task_func

libs

  • numpy
  • sklearn

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Source and history

task_id
71898bf0-60b2-51b0-b603-aa17c3634319
task_key
default--v0~2e1~2e0~5fhf--71898bf0-60b2-51b0-b603-aa17c3634319
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import