Why proper evaluation prevents overfitting in ML Python - Test Your Understanding

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'

Across_val_score

BStandardScaler

CGridSearchCV

Dtrain_test_split

Attempts:

3 left

💡 Hint

Common Mistakes

Using cross_val_score instead of train_test_split for splitting data.

Forgetting to import the correct function.

The train_test_split function splits data into training and testing sets, which is essential for proper evaluation to prevent overfitting.

2fill in blank

medium

Complete the code to train a model on the training data.

ML Python

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.[1](X_train, y_train)

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

Apredict

Bfit

Cscore

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of fit to train the model.

Using score before training the model.

The fit method trains the model using the training data.

3fill in blank

hard

Fix the error in the code to evaluate the model correctly on test data.

ML Python

accuracy = model.[1](X_test, y_test)
print(f"Test accuracy: {accuracy:.2f}")

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

Atransform

Bpredict

Cscore

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of score to get accuracy.

Trying to fit the model on test data.

The score method returns the accuracy of the model on test data, which is the correct way to evaluate performance.

4fill in blank

hard

Fill both blanks to create a dictionary of accuracy scores for training and testing data.

ML Python

scores = {
    'train_accuracy': model.[1](X_train, y_train),
    'test_accuracy': model.[2](X_test, y_test)
}

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

Ascore

Bpredict

Cfit

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit or predict instead of score for accuracy.

Using different methods for train and test accuracy.

The score method calculates accuracy on both training and testing data to compare performance and detect overfitting.

5fill in blank

hard

Fill all three blanks to perform cross-validation and print the mean accuracy.

ML Python

from sklearn.model_selection import [1]
scores = cross_val_score(model, X, y, cv=[2])
print(f"Mean CV accuracy: {scores.[3]():.2f}")

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

Across_val_score

B5

Cmean

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit instead of cross_val_score.

Setting cv to a wrong value like a string.

Trying to print scores without calculating mean.

cross_val_score performs cross-validation, cv=5 sets 5 folds, and mean() calculates the average accuracy to evaluate model generalization and prevent overfitting.

Practice

1. Why is it important to evaluate a machine learning model on data it has never seen before?

easy

A. To check if the model can make good predictions on new data

B. To make the training process faster

C. To increase the size of the training data

D. To reduce the number of features in the data

Solution

1. Step 1: Understand model evaluation purpose

   Evaluating on unseen data tests if the model generalizes beyond training examples.

2. Step 2: Identify the goal of evaluation

   The goal is to ensure the model predicts well on new, real-world data, not just memorized training data.

3. Final Answer:

   To check if the model can make good predictions on new data -> Option A

4. Quick Check:

   Evaluation on new data = Check generalization [OK]

Hint: Test on new data to check real-world performance [OK]

Common Mistakes:

• Thinking evaluation speeds up training
• Confusing evaluation with feature selection
• Believing evaluation increases training data size

2. Which of the following is the correct way to split data for proper model evaluation?

easy

A. Split data into training and testing sets

B. Use only testing data for training

C. Use all data for training and testing

D. Train on test data and evaluate on training data

Solution

1. Step 1: Recall data splitting best practice

   Proper evaluation requires separating data into training and testing sets to avoid bias.

2. Step 2: Identify correct splitting method

   Splitting data into training and testing sets ensures the model is evaluated on unseen data.

3. Final Answer:

   Split data into training and testing sets -> Option A

4. Quick Check:

   Data split = Training + Testing [OK]

Hint: Always keep test data separate from training data [OK]

Common Mistakes:

• Training and testing on the same data
• Using test data for training
• Not splitting data at all

3. Consider this Python code snippet for evaluating a model:

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

X = [[1], [2], [3], [4], [5], [6]]
y = [0, 0, 0, 1, 1, 1]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(round(accuracy, 2))

What will be the printed accuracy?

medium

A. 0.0

B. 0.67

C. 0.5

D. 1.0

Solution

1. Step 1: Understand data split and model training

   Data is split with 33% test size, so test set has 2 samples. Model trains on 4 samples.

2. Step 2: Predict and calculate accuracy

   With logistic regression on this split and data, the model predicts 1 out of 2 test samples correctly, giving accuracy 0.5.

3. Final Answer:

   0.5 -> Option C

4. Quick Check:

   Correct predictions / total test samples = 0.5 [OK]

Hint: Check test set size and prediction correctness [OK]

Common Mistakes:

• Assuming accuracy is always less than 1
• Ignoring test set size
• Confusing train and test data

4. You trained a model and got 100% accuracy on training data but only 50% on test data. What is the most likely problem?

medium

A. The training data is too small

B. The model is underfitting the training data

C. The test data is too easy

D. The model is overfitting the training data

Solution

1. Step 1: Analyze accuracy difference

   Perfect training accuracy but low test accuracy suggests the model memorized training data but fails on new data.

2. Step 2: Identify overfitting

   This behavior is classic overfitting, where the model fits training data too closely and does not generalize.

3. Final Answer:

   The model is overfitting the training data -> Option D

4. Quick Check:

   High train accuracy + low test accuracy = Overfitting [OK]

Hint: Big train-test accuracy gap means overfitting [OK]

Common Mistakes:

• Confusing overfitting with underfitting
• Blaming test data difficulty
• Ignoring accuracy differences

5. You have a dataset with 1000 samples. You want to prevent overfitting and get a reliable estimate of model performance. Which evaluation strategy is best?

hard

A. Split data once into 90% train and 10% test

B. Use 5-fold cross-validation to train and evaluate

C. Train on all data and report training accuracy

D. Evaluate only on training data with early stopping

Solution

1. Step 1: Understand evaluation strategies

   Training on all data or evaluating only on training data risks overfitting and unreliable performance estimates.

2. Step 2: Compare splitting vs cross-validation

   Single split may give biased results depending on the split. 5-fold cross-validation averages results over 5 splits, reducing bias and variance.

3. Step 3: Choose best method for reliable evaluation

   5-fold cross-validation is best to prevent overfitting and get trustworthy performance estimates on limited data.

4. Final Answer:

   Use 5-fold cross-validation to train and evaluate -> Option B

5. Quick Check:

   Cross-validation = Reliable evaluation [OK]

Hint: Use cross-validation for reliable performance estimates [OK]

Common Mistakes:

• Using training accuracy as performance
• Relying on a single train-test split
• Ignoring overfitting risks