Decision tree classifier in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

How does a decision tree classifier decide the best split?

Imagine you want to split your data at a node in a decision tree. What criterion does the tree use to choose the best feature and value to split on?

A It splits to create the largest possible groups regardless of class labels.

B It randomly picks any feature and value without considering the data.

C It chooses the split that maximizes the information gain or reduces impurity the most.

D It always splits on the feature with the highest numeric value.

❓ Predict Output

intermediate

Output of training a decision tree on simple data

What is the accuracy printed by this code after training a decision tree on the given data?

ML Python

from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

X = [[0], [1], [2], [3]]
y = [0, 0, 1, 1]

model = DecisionTreeClassifier(random_state=42)
model.fit(X, y)
pred = model.predict(X)
acc = accuracy_score(y, pred)
print(acc)

A 1.0

B 0.5

C 0.75

D 0.0

❓ Hyperparameter

advanced

Effect of max_depth on decision tree complexity

What happens if you set the max_depth parameter of a decision tree classifier to a very small number like 1?

A The tree will randomly choose splits without considering max_depth.

B The tree will grow very deep and overfit the data.

C The tree will ignore the max_depth and grow fully.

D The tree will be very simple, possibly underfitting the data.

❓ Metrics

advanced

Choosing the right metric for imbalanced classes

You trained a decision tree classifier on a dataset where 95% of samples belong to class A and 5% to class B. Which metric is best to evaluate your model's performance?

A Precision and recall, because they focus on minority class performance.

B Accuracy, because it shows overall correct predictions.

C Mean squared error, because it measures prediction error.

D Confusion matrix size, because bigger matrices mean better models.

🔧 Debug

expert

Why does this decision tree code raise an error?

What error does this code raise and why?

from sklearn.tree import DecisionTreeClassifier

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

model = DecisionTreeClassifier()
model.fit(X, y)

A TypeError: DecisionTreeClassifier() got an unexpected keyword argument.

B ValueError: Found input variables with inconsistent numbers of samples because X has 2 samples but y has 3 labels.

C IndexError: list index out of range during fitting.

D No error, the model fits successfully.