Data Science with Python Supervised Learning Models Questions and Answers

Question 1

A data scientist is working on a binary classification problem to predict customer churn.
The dataset is highly imbalanced.
After training a Random Forest and a Gradient Boosting model, they find that the Gradient Boosting model has higher accuracy.
Which of the following is the most likely reason for this outcome?

Accepted Answer

Gradient Boosting builds trees sequentially, with each tree focusing on correcting the errors of the previous one, which can be particularly effective for the minority class.

Answer

Random Forest builds trees in parallel, which is less effective on imbalanced data than the sequential approach of Gradient Boosting.

Answer

Random Forest is inherently a regression algorithm and is less suited for classification tasks.

Answer

Gradient Boosting is less prone to overfitting than Random Forest, especially on noisy datasets.

Question 2

Which of the following is a key assumption of Linear Regression that, if violated, can lead to unreliable and biased coefficient estimates?

Accepted Answer

The residuals (error terms) are independent of each other.

Answer

The dependent variable must be categorical.

Answer

The independent variables must be perfectly correlated with each other.

Answer

The relationship between the independent and dependent variables is non-linear.

Question 3

In a Support Vector Machine (SVM) model, what is the primary purpose of the 'kernel trick'?

Accepted Answer

To efficiently compute dot products in a high-dimensional feature space without explicitly mapping the data, allowing the model to find non-linear decision boundaries.

Answer

To reduce the number of features in the dataset before training the model.

Answer

To transform the data into a lower-dimensional space to make it linearly separable.

Answer

To select the most important support vectors that define the margin.

Question 4

A team is developing a model to classify news articles into one of three categories: 'Sports', 'Politics', or 'Technology'. The output variable is categorical. Which supervised learning algorithm is most appropriate for this task?

Accepted Answer

Logistic Regression

Answer

Simple Linear Regression

Answer

K-Means Clustering

Answer

Principal Component Analysis (PCA)

Question 5

An unconstrained Decision Tree is trained on a complex dataset and achieves 100% accuracy on the training data but performs poorly on the test data. What is this phenomenon called, and what is a common way to mitigate it in Scikit-learn?

Accepted Answer

Overfitting; set the `max_depth` parameter to a smaller integer.

Answer

Underfitting; decrease the `min_samples_leaf` parameter.

Answer

Bias-Variance Tradeoff; increase the number of features.

Answer

Curse of Dimensionality; use a more complex model.

Question 6

The K-Nearest Neighbors (KNN) algorithm is often referred to as a 'lazy learner'. Why is this designation used?

Accepted Answer

It does not learn a discriminative function from the training data but instead memorizes the entire training dataset.

Answer

It is computationally inefficient and slow to train compared to other algorithms.

Answer

The model's performance degrades as the size of the training data increases.

Answer

It can only be used for regression tasks and not for classification.