Overfitting and underfitting in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to split data into training and testing sets.

ML Python

from sklearn.model_selection import [1]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

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

Atrain_test_split

Bcross_val_score

CGridSearchCV

DStandardScaler

Attempts:

3 left

💡 Hint

Common Mistakes

Using a function that does not split data, like cross_val_score.

Confusing scaler functions with splitting functions.

The function train_test_split is used to split data into training and testing sets.

2fill in blank

medium

Complete the code to create a simple linear regression model.

ML Python

from sklearn.linear_model import [1]

model = LinearRegression()

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

ADecisionTreeClassifier

BLinearRegression

CLogisticRegression

DKNeighborsClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Using classification models instead of regression.

Confusing logistic regression with linear regression.

LinearRegression is the class used to create a linear regression model.

3fill in blank

hard

Fix the error in the code to calculate mean squared error correctly.

ML Python

from sklearn.metrics import mean_squared_error

mse = mean_squared_error(y_true, [1])

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

Ay_pred

By_train

CX_test

Dmodel

Attempts:

3 left

💡 Hint

Common Mistakes

Passing training labels instead of predictions.

Passing input features instead of predictions.

The mean_squared_error function compares true values y_true with predicted values y_pred.

4fill in blank

hard

Fill both blanks to create a dictionary of squared errors for errors greater than 1.

ML Python

squared_errors = {i: (y_true[i] - y_pred[i])[1]2 for i in range(len(y_true)) if (y_true[i] - y_pred[i]) [2] 1}

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

A**

B>

C<

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition instead of power for squaring.

Using less than instead of greater than for filtering.

The ** operator is used to square the error, and > checks if the error is greater than 1.

5fill in blank

hard

Fill all three blanks to create a dictionary of errors for predictions less than 0.5.

ML Python

errors = {i: y_true[i] [1] y_pred[i] for i in range(len(y_true)) if y_pred[i] [2] 0.5 and i [3] 10}

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

A-

B<

C<=

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition instead of subtraction for error.

Using greater than instead of less than for filtering.

The error is calculated by subtracting predicted from true values, filtering predictions less than 0.5, and indices less than or equal to 10.

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