Learning curves in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function that plots learning curves from sklearn.

ML Python

from sklearn.model_selection import [1]

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

Alearning_curve

Bcross_val_score

Ctrain_test_split

DGridSearchCV

Attempts:

3 left

💡 Hint

Common Mistakes

Importing unrelated functions like train_test_split.

Confusing cross-validation functions with learning curve functions.

The learning_curve function is used to generate data for plotting learning curves.

2fill in blank

medium

Complete the code to generate training and validation scores for a model using learning_curve.

ML Python

train_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=[1])

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

A0.5

B100

C5

D'auto'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a float value for cv instead of an integer.

Setting cv to a very large number which is inefficient.

The cv parameter specifies the number of folds for cross-validation, commonly 5 or 10.

3fill in blank

hard

Fix the error in the code to correctly compute the mean training scores.

ML Python

mean_train_scores = train_scores.[1](axis=1)

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

Amean

Bsum

Cmean(axis=0)

Daverage

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum instead of mean, which gives total not average.

Passing axis=0 which averages the wrong dimension.

The mean method computes the average along the specified axis. Here, axis=1 averages across folds.

4fill in blank

hard

Fill both blanks to plot training and validation scores against training sizes.

ML Python

plt.plot(train_sizes, mean_train_scores, label='[1]')
plt.plot(train_sizes, mean_test_scores, label='[2]')

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

ATraining score

BValidation score

CTest score

DAccuracy

Attempts:

3 left

💡 Hint

Common Mistakes

Labeling validation scores as 'Test score' which is less clear.

Using the same label for both lines.

Training scores are plotted with label 'Training score' and validation scores with 'Validation score' to distinguish them clearly.

5fill in blank

hard

Fill all three blanks to complete the code that plots learning curves with labels and axis titles.

ML Python

plt.xlabel('[1]')
plt.ylabel('[2]')
plt.title('[3]')

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

ATraining examples

BScore

CLearning Curve

DAccuracy

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Accuracy' as y-axis label which is too specific.

Omitting the plot title.

The x-axis label should be 'Training examples', y-axis label 'Score', and the plot title 'Learning Curve' for clarity.

Practice

1. What does a learning curve primarily show in machine learning?

easy

A. The hardware used for training the model

B. How model performance changes with more training data

C. The number of features in the dataset

D. The exact time a model takes to train

Solution

1. Step 1: Understand the purpose of learning curves

   Learning curves plot model performance (like accuracy or error) against the amount of training data.

2. Step 2: Identify what is shown on the curve

   The curve shows how well the model learns as it sees more data, helping to spot underfitting or overfitting.

3. Final Answer:

   How model performance changes with more training data -> Option B

4. Quick Check:

   Learning curve = model performance vs data size [OK]

Hint: Learning curves show performance vs data size [OK]

Common Mistakes:

• Confusing learning curves with training time plots
• Thinking learning curves show feature counts
• Assuming hardware affects learning curves directly

2. Which of the following is the correct way to plot a learning curve in Python using scikit-learn?

easy

A. import sklearn.plot as sp; sp.learning_curve(model, X, y)

B. from sklearn.learning import curve_plot; curve_plot(model, X, y)

C. from sklearn.model_selection import learning_curve; train_sizes, train_scores, test_scores = learning_curve(model, X, y)

D. from sklearn.model_selection import train_curve; train_curve(model, X, y)

Solution

1. Step 1: Recall the correct scikit-learn function for learning curves

   The function is learning_curve from sklearn.model_selection.

2. Step 2: Check the import and usage syntax

   from sklearn.model_selection import learning_curve; train_sizes, train_scores, test_scores = learning_curve(model, X, y) correctly imports learning_curve and calls it with model, features X, and labels y.

3. Final Answer:

   from sklearn.model_selection import learning_curve; train_sizes, train_scores, test_scores = learning_curve(model, X, y) -> Option C

4. Quick Check:

   Correct import and call = from sklearn.model_selection import learning_curve; train_sizes, train_scores, test_scores = learning_curve(model, X, y) [OK]

Hint: Use sklearn.model_selection.learning_curve to plot learning curves [OK]

Common Mistakes:

• Using wrong module or function names
• Trying to import non-existent functions
• Confusing learning_curve with train_test_split

3. Given this code snippet, what will be the shape of train_sizes if X has 100 samples?

from sklearn.model_selection import learning_curve
train_sizes, train_scores, test_scores = learning_curve(model, X, y, train_sizes=[0.1, 0.5, 1.0])

medium

A. (100,)

B. (1,)

C. (3, 100)

D. (3,)

Solution

1. Step 1: Understand train_sizes parameter

   train_sizes is given as a list with 3 values: 0.1, 0.5, and 1.0, representing fractions of the training data.

2. Step 2: Determine output shape of train_sizes

   The learning_curve function returns train_sizes as an array with length equal to the number of training sizes specified, so shape is (3,).

3. Final Answer:

   (3,) -> Option D

4. Quick Check:

   train_sizes length = number of fractions given [OK]

Hint: train_sizes shape matches number of training fractions [OK]

Common Mistakes:

• Assuming train_sizes matches number of samples
• Confusing train_sizes with train_scores shape
• Thinking train_sizes is always (100,)

4. You plotted a learning curve but see both training and validation errors are high and close. What is the likely issue?

medium

A. The model is underfitting the data

B. The model is overfitting the data

C. There is a data leakage problem

D. The training data is too large

Solution

1. Step 1: Analyze error levels on learning curve

   High training error means the model cannot fit the training data well.

2. Step 2: Compare training and validation errors

   Both errors being high and close indicates underfitting, where the model is too simple.

3. Final Answer:

   The model is underfitting the data -> Option A

4. Quick Check:

   High train & val error = underfitting [OK]

Hint: High train and val errors mean underfitting [OK]

Common Mistakes:

• Confusing underfitting with overfitting
• Assuming data leakage causes high errors
• Thinking large data size causes this pattern

5. You have a learning curve where training error is very low but validation error is high and does not improve with more data. What is the best next step?

hard

A. Try a simpler model to reduce overfitting

B. Collect more training data to improve validation

C. Increase model complexity to fit training data better

D. Stop training because the model is perfect

Solution

1. Step 1: Interpret the learning curve pattern

   Low training error but high validation error means the model fits training data too well but fails on new data, indicating overfitting.

2. Step 2: Decide the best action to fix overfitting

   Using a simpler model reduces complexity and helps generalize better, improving validation error.

3. Final Answer:

   Try a simpler model to reduce overfitting -> Option A

4. Quick Check:

   Low train error + high val error = overfitting; simplify model [OK]

Hint: Low train error + high val error means overfitting; simplify model [OK]

Common Mistakes:

• Collecting more data won't fix overfitting alone
• Increasing complexity worsens overfitting
• Assuming model is perfect with low training error