Which ML Model to Use for Predictive Analysis in Ecommerce? | AWS Certified Big Data - Specialty Exam
Question
A company is planning on using the Machine Learning service to perform a predictive analysis.
This is for a ecommerce application wherein, based on various metrics they want to determine if a person would probably buy a product or not.
Which of the following ML model would be used for this requirement?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer - C.
Since this is a simple case of a Yes or No classification, you should use the Binary classification.
Hence all other options are invalid.
The different types of ML models are given in the AWS Documentation.
ML Models.
An ML model is a mathematical model that generates predictions by finding patterns in your data.
Amazon ML supports three types of ML models: binary classification, multiclass classification and regression.
The following table defines terms that are related to ML models.
Term Definition.
Regression The goal of training a regression ML model is to predict a numeric value.
Multiclass The goal of training a multiclass ML model is to predict values that belong to a limited, pre-defined set of permissible values.
Binary The goal of training a binary ML model is to predict values that can only have one of two states, such as true or false.
Model Size ML models capture and store patterns.
The more patterns a ML model stores, the bigger it will be.
ML model size is described in Mbytes.
Number of Passes When you train an ML model, you use data from a datasource.
It is sometimes beneficial to use each data record in the learning process more than once.
The number of times that you let Amazon ML use the same data records is called the number of passes.
Regularization Regularization is a machine learning technique that you can use to obtain higher-quality models.
Amazon ML offers a default setting that works well for most cases.
For more information on Machine Learning Key concepts, please refer to the below URL.
https://docs.aws.amazon.com/machine-learning/latest/dg/amazon-machine-learning-key-concepts.htmlThe correct answer for this question is C. Binary.
Binary classification is a type of supervised machine learning algorithm that is used to predict a binary outcome. In this case, the outcome is whether a person would probably buy a product or not. The algorithm is trained on a set of data that includes both positive and negative examples of the outcome, and it learns to classify new examples as either positive or negative based on the patterns in the data.
Regression is a type of supervised machine learning algorithm that is used to predict a continuous value. It would not be appropriate for this scenario as we are interested in predicting a binary outcome (buy or not buy).
Multiclass classification is a type of supervised machine learning algorithm that is used to predict one of several possible outcomes. It would not be appropriate for this scenario as we are only interested in predicting one of two possible outcomes (buy or not buy).
Model size is not a type of machine learning algorithm. It is a factor that can affect the performance of machine learning models, but it is not directly related to the type of model that would be used in this scenario.
Therefore, the correct answer is C. Binary.
