(editing) Data Science Interview Prep - ML Basic

August 9, 2024 1 minute read

Interview Questions & Answers

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How would you develop a machine learning model to predict whether or not a customer will purchase a product?

To develop a machine learning model for predicting customer purchases, you’d start by collecting and preprocessing relevant data, such as past purchase history, customer demographics, and engagement metrics. Training a model on this dataset with a binary outcome (purchase or not) enables the identification of patterns and factors influencing purchase decisions.

What is a decision tree?

A decision tree is a machine-learning model used to predict the value of a target variable. It splits the dataset into smaller subsets, each containing only one data point.

How would you use a decision tree to predict whether or not a customer will churn?

To predict customer churn using a decision tree, training the model must be done with data on previously churned customers. This involves analyzing customer behavior and attributes to identify patterns. Once trained, the model can predict the likelihood of future customers churning.

What is gradient boosting?

Gradient boosting is a machine learning algorithm designed to improve accuracy by sequentially training weak models. Each model corrects errors made by the previous ones, combining their predictions to form a more accurate final prediction.

How would you use gradient boosting to improve the accuracy of a machine-learning model?

Improving a model’s accuracy with gradient boosting involves training weak models on different data subsets and iteratively adjusting them based on the accuracy of the previous model’s predictions. This process reduces bias and variance, leading to a more accurate and robust model.