Model comparison strategies in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Cross-Validation Purpose

Why do we use cross-validation when comparing machine learning models?

A To estimate model performance on unseen data by splitting data into multiple train-test sets

B To increase the training data size by duplicating samples

C To reduce the number of features in the dataset

D To speed up the training process by using smaller datasets

💡 Hint

Think about how to check if a model will work well on new data.

Cross-validation splits data into several parts to train and test models multiple times. This helps estimate how well the model will perform on new, unseen data.

❓ Metrics

intermediate

Choosing the Best Metric for Imbalanced Data

You have a classification problem with very imbalanced classes. Which metric is best to compare models fairly?

A F1-score

B Mean Squared Error

C Precision

D Accuracy

💡 Hint

Think about a metric that balances false positives and false negatives.

F1-score balances precision and recall, making it suitable for imbalanced datasets where accuracy can be misleading.

❓ Predict Output

advanced

Output of Model Comparison Using Cross-Validation Scores

What is the output of the following Python code?

ML Python

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

iris = load_iris()
X, y = iris.data, iris.target
model = DecisionTreeClassifier(random_state=42)
scores = cross_val_score(model, X, y, cv=5)
print(round(scores.mean(), 2))

A 0.75

B 0.85

C 0.95

D 0.65

💡 Hint

Decision trees usually perform well on iris dataset; cross-validation averages accuracy over 5 folds.

The DecisionTreeClassifier on iris dataset with 5-fold cross-validation typically achieves around 95% accuracy.

❓ Hyperparameter

advanced

Effect of Increasing Number of Folds in Cross-Validation

What is the main effect of increasing the number of folds (cv) in k-fold cross-validation?

A It increases bias and decreases variance of the performance estimate

B It decreases bias and increases variance of the performance estimate

C It decreases both bias and variance of the performance estimate

D It increases both bias and variance of the performance estimate

💡 Hint

More folds mean training on more data each time but testing on fewer samples.

Increasing folds reduces bias because models train on more data each time, but variance increases because test sets are smaller and results vary more.

🔧 Debug

expert

Identifying the Error in Model Comparison Code

What error does the following code raise when trying to compare two models using cross-validation?

ML Python

from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import cross_val_score

iris = load_iris()
X, y = iris.data, iris.target

model1 = LogisticRegression(max_iter=200)
model2 = DecisionTreeClassifier()

scores1 = cross_val_score(model1, X, y, cv=5)
scores2 = cross_val_score(model2, X, y, cv=5)

print(scores1.mean())
print(scores2.mean())

A NameError because model2 is not defined

B TypeError because cross_val_score expects a list of models

C ValueError because iris dataset has missing values

D ConvergenceWarning due to LogisticRegression default max_iter too low

💡 Hint

Check if LogisticRegression needs extra parameters for convergence.

LogisticRegression by default has max_iter=100 which may be too low for convergence on iris data, causing a ConvergenceWarning.

Practice

1. What is the main purpose of model comparison in machine learning?

easy

A. To increase the size of the training data

B. To reduce the number of features in the dataset

C. To find the best model for the task using fair evaluation

D. To speed up the training process

Solution

1. Step 1: Understand model comparison goal

   Model comparison aims to select the best model by evaluating multiple models on the same task.

2. Step 2: Identify correct purpose

   Increasing data size, reducing features, or speeding training are not the main goals of model comparison.

3. Final Answer:

   To find the best model for the task using fair evaluation -> Option C

4. Quick Check:

   Model comparison = best model selection [OK]

Hint: Model comparison picks the best model fairly [OK]

Common Mistakes:

• Confusing model comparison with data preprocessing
• Thinking it speeds up training
• Mixing it with feature selection

2. Which of the following is the correct way to compare two models using accuracy in Python?

easy

A. print(accuracy_score(y_true, model1.predict(X)) > accuracy_score(y_true, model2.predict(X)))

B. print(model1.accuracy > model2.accuracy)

C. print(model1.predict(X) > model2.predict(X))

D. print(model1.score(X_train) == model2.score(X_test))

Solution

1. Step 1: Understand accuracy comparison

   Accuracy should be computed using true labels and predictions, not model attributes directly.

2. Step 2: Check each option

   print(accuracy_score(y_true, model1.predict(X)) > accuracy_score(y_true, model2.predict(X))) correctly uses accuracy_score with true labels and predictions for both models.

3. Final Answer:

   print(accuracy_score(y_true, model1.predict(X)) > accuracy_score(y_true, model2.predict(X))) -> Option A

4. Quick Check:

   Use accuracy_score with true labels and predictions [OK]

Hint: Compare metrics computed on true labels and predictions [OK]

Common Mistakes:

• Comparing model attributes directly
• Comparing predictions without labels
• Mixing train and test data in scoring

3. Given the code below, what will be printed?

from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression

model1 = LogisticRegression()
model2 = LogisticRegression(C=0.1)

scores1 = cross_val_score(model1, X, y, cv=5)
scores2 = cross_val_score(model2, X, y, cv=5)

print(scores1.mean() > scores2.mean())

medium

A. False if model1 has higher average cross-validation score

B. True if model1 has higher average cross-validation score

C. Always True

D. SyntaxError due to missing imports

Solution

1. Step 1: Understand cross_val_score output

   cross_val_score returns an array of scores for each fold; mean() gives average performance.

2. Step 2: Compare mean scores

   The print statement compares which model has higher average score and prints True or False accordingly.

3. Final Answer:

   True if model1 has higher average cross-validation score -> Option B

4. Quick Check:

   Mean cross-val scores compared, prints True if model1 better [OK]

Hint: Cross-validation mean score comparison prints True if first is better [OK]

Common Mistakes:

• Assuming print always outputs True
• Confusing cross_val_score output with single score
• Missing that mean() is needed for average

4. You run this code to compare two models but get an error:

from sklearn.metrics import accuracy_score

acc1 = accuracy_score(y_true, model1.predict(X_test))
acc2 = accuracy_score(y_true, model2.predict(X_test))

if acc1 > acc2:
    print('Model 1 is better')
else
    print('Model 2 is better')

What is the error and how to fix it?

medium

A. IndentationError due to wrong indent; fix indentation

B. NameError because y_true is undefined; define y_true

C. TypeError because predict returns probabilities; use predict_classes

D. SyntaxError due to missing colon after else; add ':' after else

Solution

1. Step 1: Identify syntax error in if-else block

   The else statement is missing a colon at the end, causing a SyntaxError.

2. Step 2: Fix syntax by adding colon

   Add ':' after else to correct the syntax and run the code successfully.

3. Final Answer:

   SyntaxError due to missing colon after else; add ':' after else -> Option D

4. Quick Check:

   Missing colon after else causes SyntaxError [OK]

Hint: Check colons after if/else statements [OK]

Common Mistakes:

• Ignoring missing colon after else
• Assuming predict returns classes always
• Confusing indentation error with syntax error

5. You want to compare three models fairly on a small dataset. Which strategy is best to get reliable results?

hard

A. Use cross-validation with the same metric for all models

B. Train models on different datasets and compare their training loss

C. Use a single train/test split and compare accuracy

D. Compare models only by their training accuracy

Solution

1. Step 1: Understand fair comparison on small data

   Single splits can be unreliable; training accuracy is biased; different datasets cause unfairness.

2. Step 2: Choose cross-validation with same metric

   Cross-validation uses multiple splits to reduce randomness and comparing with the same metric ensures fairness.

3. Final Answer:

   Use cross-validation with the same metric for all models -> Option A

4. Quick Check:

   Cross-validation + same metric = reliable fair comparison [OK]

Hint: Cross-validation with same metric ensures fair comparison [OK]

Common Mistakes:

• Using single train/test split only
• Comparing training accuracy instead of test
• Using different datasets for each model