Overfitting and underfitting in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Identifying Overfitting from Model Behavior

You train a model on a dataset. After training, you notice the training accuracy is 98%, but the test accuracy is only 65%. What does this most likely indicate?

A The model is underfitting the data and needs more training.

B The model is overfitting the training data and does not generalize well.

C The model has perfect generalization and is well balanced.

D The test data is too easy compared to training data.

💡 Hint

Think about what it means when training accuracy is very high but test accuracy is much lower.

When training accuracy is very high but test accuracy is low, the model has learned the training data too well, including noise or details that don't apply to new data. This is called overfitting.

❓ Predict Output

intermediate

Output of Training and Validation Loss Curves

Consider the following training and validation loss values over epochs for a model:

Epoch 1: train_loss=0.8, val_loss=0.9
Epoch 2: train_loss=0.5, val_loss=0.6
Epoch 3: train_loss=0.3, val_loss=0.7
Epoch 4: train_loss=0.1, val_loss=1.2
Epoch 5: train_loss=0.05, val_loss=1.5

What does this pattern most likely indicate about the model?

A The model has a data leak causing validation loss to increase.

B The model is underfitting because both losses are high.

C The model is perfectly fitting the data with balanced losses.

D The model is overfitting because training loss decreases but validation loss increases.

💡 Hint

Look at how training loss and validation loss change relative to each other.

Training loss keeps decreasing, but validation loss starts increasing after epoch 2. This means the model fits training data too closely and performs worse on new data, a sign of overfitting.

❓ Model Choice

advanced

Choosing a Model to Avoid Underfitting

You have a complex dataset with many features and nonlinear relationships. Which model choice is least likely to underfit this data?

A A deep neural network with multiple hidden layers.

B A logistic regression model without feature interactions.

C A simple linear regression model with few features.

D A decision tree with maximum depth of 2.

💡 Hint

Think about model complexity and ability to capture nonlinear patterns.

A deep neural network with multiple layers can learn complex nonlinear relationships, reducing underfitting risk. Simpler models may not capture the data complexity well.

❓ Hyperparameter

advanced

Hyperparameter to Control Overfitting in Neural Networks

Which hyperparameter adjustment is most effective to reduce overfitting in a neural network?

A Increasing the learning rate to speed up training.

B Increasing the number of epochs to train longer.

C Adding dropout layers to randomly disable neurons during training.

D Removing regularization to allow the model to fit training data better.

💡 Hint

Consider techniques that prevent the model from relying too much on specific neurons.

Dropout randomly disables neurons during training, forcing the network to learn more robust features and reducing overfitting. Increasing epochs or learning rate can worsen overfitting.

❓ Metrics

expert

Interpreting Metrics to Detect Underfitting

A model trained on a dataset shows the following metrics:

Training accuracy: 60%
Validation accuracy: 58%

Which conclusion is most accurate?

A The model is underfitting because both training and validation accuracies are low and close.

B The model is perfectly fitted with balanced accuracies.

C The model is overfitting because training accuracy is much higher than validation accuracy.

D The model has data leakage causing similar accuracies.

💡 Hint

Look at the absolute values and difference between training and validation accuracies.

Both accuracies are low and close, indicating the model is not learning the data well (underfitting). Overfitting would show high training accuracy and low validation accuracy.

Practice

1. What does overfitting mean in machine learning?

easy

A. The model is too simple and misses important patterns

B. The model learns noise and details too well, failing on new data

C. The model perfectly predicts new data without errors

D. The model ignores training data completely

Solution

1. Step 1: Understand overfitting definition

   Overfitting happens when a model learns not only the true patterns but also the noise and random details in training data.

2. Step 2: Match definition to options

   The model learns noise and details too well, failing on new data describes this exactly, while others describe underfitting or unrelated concepts.

3. Final Answer:

   The model learns noise and details too well, failing on new data -> Option B

4. Quick Check:

   Overfitting = learning noise too well [OK]

Hint: Overfitting means memorizing training noise, not generalizing [OK]

Common Mistakes:

• Confusing overfitting with underfitting
• Thinking overfitting means perfect prediction on new data
• Believing overfitting ignores training data

2. Which of the following is a sign of underfitting in a model?

easy

A. High training error but low test error

B. Low training error but high test error

C. Low training error and low test error

D. High training error and high test error

Solution

1. Step 1: Understand underfitting error patterns

   Underfitting means the model is too simple, so it performs poorly on both training and test data, causing high errors.

2. Step 2: Match error patterns to options

   High training error and high test error shows high errors on both training and test sets, which matches underfitting.

3. Final Answer:

   High training error and high test error -> Option D

4. Quick Check:

   Underfitting = high error everywhere [OK]

Hint: Underfitting means bad on training and test data [OK]

Common Mistakes:

• Confusing underfitting with overfitting error patterns
• Assuming low training error means no underfitting
• Ignoring test error when diagnosing underfitting

3. Consider this Python code training a model:

from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

model = DecisionTreeClassifier(max_depth=1)
model.fit(X_train, y_train)
train_score = model.score(X_train, y_train)
test_score = model.score(X_test, y_test)
print(round(train_score, 2), round(test_score, 2))

What is the likely output?

medium

A. 0.60 0.58

B. 0.95 0.90

C. 1.00 0.50

D. 0.80 0.80

Solution

1. Step 1: Understand model complexity and max_depth=1

   A decision tree with max_depth=1 is very simple, likely underfitting the iris data, so training accuracy will be low.

2. Step 2: Predict scores based on underfitting

   Both training and test scores will be low and close, around 0.6, matching 0.60 0.58.

3. Final Answer:

   0.60 0.58 -> Option A

4. Quick Check:

   Simple model = low train and test scores [OK]

Hint: Low max_depth means underfitting, expect low accuracy [OK]

Common Mistakes:

• Assuming max_depth=1 gives perfect accuracy
• Confusing training and test scores
• Ignoring model simplicity effect

4. This code tries to reduce overfitting but has a mistake:

from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

model = DecisionTreeClassifier(max_depth=10)
model.fit(X_train, y_train)
print(model.score(X_train, y_train))
print(model.score(X_test, y_test))

# To reduce overfitting, we set max_depth=1
model.max_depth = 1
print(model.score(X_train, y_train))
print(model.score(X_test, y_test))

What is the error in this approach?

medium

A. Changing max_depth after fitting does not affect the model

B. max_depth=1 causes syntax error

C. fit() method cannot be called twice

D. max_depth must be set to None to reduce overfitting

Solution

1. Step 1: Understand how max_depth affects model

   max_depth is a parameter set when creating the model. Changing it after fitting does not change the trained model.

2. Step 2: Identify mistake in code

   Setting model.max_depth=1 after fitting does not reduce overfitting. The model must be recreated with max_depth=1 before fitting.

3. Final Answer:

   Changing max_depth after fitting does not affect the model -> Option A

4. Quick Check:

   Parameters must be set before fit() [OK]

Hint: Set parameters before fit(), changing after does nothing [OK]

Common Mistakes:

• Thinking changing max_depth after fit changes model
• Believing fit() can only be called once
• Confusing parameter setting with method calls

5. You have a model that overfits training data. Which combination of techniques can help reduce overfitting effectively?

hard

A. Increase model complexity and reduce training data

B. Remove validation set and train longer

C. Use regularization and increase training data size

D. Use fewer features and increase learning rate

Solution

1. Step 1: Identify techniques to reduce overfitting

   Overfitting can be reduced by regularization (penalties to simplify model) and by increasing training data to help generalize better.

2. Step 2: Evaluate options

   Use regularization and increase training data size correctly combines regularization and more data. Other options either increase complexity or remove validation, which worsen overfitting.

3. Final Answer:

   Use regularization and increase training data size -> Option C

4. Quick Check:

   Regularization + more data = less overfitting [OK]

Hint: Regularize and add data to fight overfitting [OK]

Common Mistakes:

• Increasing complexity to fix overfitting
• Ignoring validation data importance
• Increasing learning rate without reason