You work as a machine learning specialist for an analytics consulting firm that produces machine learning models for businesses that wish to understand the effects of social media on their product sales.
Your latest assignment is to build a model that predicts whether a user will click-through an advertisement on a set of social media apps.
For this problem, you have hundreds of millions of observations with hundreds of features.
Which type of algorithm should you use to meet your business problem?
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: D.
Option A is incorrect.
A neural network with a small number of hidden layers will not perform well with hundreds of millions of observations with hundreds of features.
Option B is incorrect.
A logistic regression algorithm will not perform well with hundreds of millions of observations with hundreds of features.
Option C is incorrect.
Clustering is not the best choice of algorithm for a problem where you are trying to solve for a binary target, click-through or not.
Option D is correct.
For a problem where you have hundreds of millions of observations with hundreds of features, a neural network with a large number of hidden layers will perform the best.
Reference:
Please see the Machine Learning Yearning by Andrew Ng, chapter 4: Scale Drives Machine Learning Progress (https://github.com/ajaymache/machine-learning-yearning), and the Towards Data Science article titled Machine Learning vs.
Deep Learning (https://towardsdatascience.com/machine-learning-vs-deep-learning-62137a1c9842), the Statistics Solutions article titled What is Logistic Regression? (https://www.statisticssolutions.com/free-resources/directory-of-statistical-analyses/what-is-logistic-regression/), the Investopedia article titled Neural Network (https://www.investopedia.com/terms/n/neuralnetwork.asp), the Amazon SageMaker developer guide titled Linear Learner Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/linear-learner.html)
The most appropriate algorithm to use for this problem is a Neural Network with a large number of hidden layers (Option D).
Neural Networks are a powerful class of machine learning algorithms that are particularly suited for solving complex problems with a large number of features. They are particularly useful in image recognition, natural language processing, and in predicting patterns in time series data.
The task at hand is to predict whether a user will click-through an advertisement on social media apps. Given that there are hundreds of millions of observations and hundreds of features, a Neural Network with a large number of hidden layers is best suited to solve this problem.
Logistic Regression (Option B) is a simpler algorithm that is often used for binary classification problems, where the goal is to predict a binary outcome such as Yes/No or True/False. While it is suitable for simple problems with few features, it may not be the best choice for this particular problem with hundreds of features.
Clustering (Option C) is a technique used to group similar data points together based on their similarity. It is not suitable for this problem since the goal is to predict whether a user will click-through an advertisement, which is a binary classification problem.
A Neural Network with a small number of hidden layers (Option A) may not be able to handle the complexity of the problem and may not be able to capture the patterns and relationships between the features. A larger number of hidden layers is needed to model the complex interactions between the features and improve the accuracy of the model.
In conclusion, the most appropriate algorithm to use for this problem is a Neural Network with a large number of hidden layers.