MLS-C01 Question 146
Single answerA retail company wants to predict whether a customer will make a purchase (binary classification) based on features like browsing history, time spent on the website, and demographic data. They also want the model to handle missing values and feature scaling automatically. Which algorithm would be the most suitable for this task?
- A
XGBoost
- B
Logistic Regression
- C
K-Means Clustering
- D
Linear Regression
Show answer and explanation
Correct answer: A
Explanation
XGBoost is the best choice for binary classification tasks where preprocessing tasks like handling missing values and feature scaling need to be automated. It is a robust and high-performing algorithm for structured data and classification problems, unlike Logistic Regression, which requires more manual preprocessing steps, or Linear Regression, which is not designed for classification tasks. K-Means is not applicable as it is used for clustering rather than supervised classification.
- A. Correct.
XGBoost is suitable for this task because it is a gradient boosting algorithm that excels at handling missing values, automates feature scaling, and performs well in binary classification tasks.
- B. Incorrect.
Logistic Regression can handle binary classification but requires manual handling of missing values and feature scaling, making it less optimal for this scenario compared to XGBoost.
- C. Incorrect.
K-Means Clustering is an unsupervised learning algorithm focused on grouping data into clusters, which is not suitable for a binary classification problem.
- D. Incorrect.
Linear Regression is a regression algorithm and is designed to predict continuous values, making it unsuitable for binary classification tasks.