Learning curves in ML Python - Model Metrics & Evaluation

Metrics & Evaluation - Learning curves

Which metric matters for Learning curves and WHY

Learning curves show how a model's performance changes as it learns from more data or training time. The key metrics are training error and validation error. These tell us if the model is improving, overfitting, or underfitting. Watching these errors helps decide if more data or a different model is needed.

Confusion matrix or equivalent visualization

Learning curves are usually shown as line graphs, not confusion matrices. Here is a simple ASCII example of a learning curve showing error rates:

    Training Error:  \    
                    \   
                     \  
                      \ 
                       \
    Validation Error:  /\  
                     /  \ 
                    /    \
                   /      \
                  /        \
    Data Size --->
    

The training error line goes down as data increases. The validation error line may go down then up if overfitting happens.

Precision vs Recall tradeoff analogy for Learning curves

Learning curves help balance underfitting and overfitting. Think of it like baking a cake:

• Underfitting = cake not baked enough (high errors on training and validation)
• Overfitting = cake burnt on outside but raw inside (low training error but high validation error)
• Good fit = cake baked just right (both errors low and close)

Learning curves show if the model is "baking" well or needs adjustment.

What "good" vs "bad" learning curves look like

Good learning curves:

• Training and validation errors both decrease and get close as data increases.
• Validation error stabilizes at a low value.

Bad learning curves:

• Training error stays high and close to validation error (underfitting).
• Training error very low but validation error high and rising (overfitting).
• Validation error fluctuates wildly (data or model issues).

Common pitfalls when using learning curves

• Ignoring data quality: More data won't help if data is noisy or wrong.
• Overfitting to validation set: Using validation data too much can mislead learning curves.
• Small data sizes: Learning curves with very little data can be noisy and unclear.
• Not enough training steps: Early stopping too soon can hide true learning behavior.

Self-check question

Your model's learning curve shows training error near zero but validation error stays high and does not improve with more data. Is this good?

Answer: No, this means the model is overfitting. It learns training data too well but fails to generalize. You should try simpler models, regularization, or more diverse data.

Key Result

Learning curves track training and validation errors to detect underfitting or overfitting and guide model improvement.

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