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BigCodeBench / BigCodeBench v0.1.0_hf d3855aa7-b018-5d29-807e-a0b3edb0cc4d

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.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

def task_func(df, test_size=0.2, random_state=42):
    """
    Predicts categories based on 'Age' and 'Score' in a given DataFrame using a Random Forest Classifier. 
    Rows with duplicate 'Name' entries are dropped before the prediction. The function uses a Random Forest Classifier 
    from sklearn to make predictions and evaluates the model using accuracy.

    Parameters:
    df (DataFrame): A pandas DataFrame with columns 'Name', 'Age', 'Score', and 'Category'.
    test_size (float, optional): Proportion of the dataset to include in the test split. Default is 0.2.
    random_state (int, optional): Controls the shuffling applied to the data before applying the split. Default is 42.

    Returns:
    float: The accuracy of the prediction as a float value.
    
    Raises:
    - The function will raise a ValueError is input df is not a DataFrame.
    
    Requirements:
    - pandas
    - sklearn.model_selection.train_test_split
    - sklearn.ensemble.RandomForestClassifier
    - sklearn.metrics.accuracy_score

    Example:
    >>> data = pd.DataFrame([{'Name': 'James', 'Age': 30, 'Score': 85, 'Category': 'Electronics'}, {'Name': 'Lily', 'Age': 28, 'Score': 92, 'Category': 'Home'}])
    >>> accuracy = task_func(data)
    >>> accuracy <= 1.0
    True
    """

instruct prompt

Predicts categories based on 'Age' and 'Score' in a given DataFrame using a Random Forest Classifier. Rows with duplicate 'Name' entries are dropped before the prediction. The function uses a Random Forest Classifier from sklearn to make predictions and evaluates the model using accuracy.
The function should raise the exception for: The function will raise a ValueError is input df is not a DataFrame.
The function should output with:
    float: The accuracy of the prediction as a float value.
You should write self-contained code starting with:

Code

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
def task_func(df, test_size=0.2, random_state=42):

code prompt

Code

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
def task_func(df, test_size=0.2, random_state=42):

entry point

task_func

libs

  • pandas
  • sklearn

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Source and history

task_id
d3855aa7-b018-5d29-807e-a0b3edb0cc4d
task_key
default--v0~2e1~2e0~5fhf--d3855aa7-b018-5d29-807e-a0b3edb0cc4d
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import