Documentation best practices in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to add a clear description to the machine learning model function.

ML Python

def train_model(data):
    """[1]"""
    # training steps here
    pass

Drag options to blanks, or click blank then click option'

ATrain a model using the given data

BCalculate the accuracy of the model

CLoad the dataset from file

DPlot the training loss graph

Attempts:

3 left

💡 Hint

Common Mistakes

Writing unrelated descriptions like plotting or loading data

Leaving the docstring empty

The docstring should describe what the function does. Here, it trains a model using the given data.

2fill in blank

medium

Complete the code to add parameter documentation for the input data.

ML Python

def train_model(data):
    """
    Train a model.

    Parameters:
        data: [1]
    """
    pass

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Alist of numbers

Bplotting function

Cmodel accuracy score

Dinput dataset for training

Attempts:

3 left

💡 Hint

Common Mistakes

Describing output or unrelated items

Using vague terms like 'list of numbers' without context

The parameter description should explain what 'data' is used for, here it is the input dataset for training.

3fill in blank

hard

Fix the error in the docstring to correctly document the return value of the function.

ML Python

def train_model(data):
    """
    Train a model.

    Returns:
        [1]: trained model object
    """
    pass

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Aaccuracy

Bloss

Cmodel

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using metric names like accuracy or loss instead of the model

Using input parameter names as return values

The return value should be named 'model' as it represents the trained model object returned by the function.

4fill in blank

hard

Fill both blanks to complete the example section in the docstring.

ML Python

def train_model(data):
    """
    Train a model.

    Example:
        model = train_model([1])
        print(model.[2]())
    """
    pass

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Atraining_data

Bscore

Cpredict

Dtest_data

Attempts:

3 left

💡 Hint

Common Mistakes

Using test data as input in the example

Using metric methods like 'score' instead of 'predict' in example

The example shows calling the function with 'training_data' and then using the 'predict' method on the model.

5fill in blank

hard

Fill all three blanks to complete the docstring with description, parameters, and returns.

ML Python

def evaluate_model(model, test_data):
    """
    [1].

    Parameters:
        model: [2]
        test_data: [3]

    Returns:
        float: accuracy score
    """
    pass

Drag options to blanks, or click blank then click option'

AEvaluate the model on test data

Ba trained machine learning model

Cdataset used for testing the model

DTrain the model with data

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing training and evaluation descriptions

Using vague parameter descriptions

The docstring describes the function purpose, parameters, and return value clearly for evaluation.