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

❓ 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

❓ 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

❓ 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

🔧 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