You are a machine learning specialist working for a credit card company where you are building a fraud detection model.
You have your model built using the XGBoost algorithm, and you are now attempting to find the best version of your model by performing automatic model tuning.
You are creating your hyperparameter tuning job, and you need to select the appropriate technique the job will use to find your best hyperparameters.
You wish to run the least number of hyperparameter tuning training jobs as possible. Which hyperparameter tuning technique is best suited to your requirements?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: A.
Option A is correct.
The Bayesian optimization approach to hyperparameter tuning results in fewer tuning job runs than the random search method.
Option B is incorrect.
The Bayesian technique used by the hyperparameter tuning job in SageMaker is Bayesian optimization, which solves the problem using regression, not classification.
Option C is incorrect.
A random search method is a valid option when using SageMaker hyperparameter tuning.
But the random search approach usually requires running many more training jobs to get the best hyperparameters when compared to the Bayesian optimization approach.
You have a requirement to run the least number of hyperparameter tuning training jobs as possible.
Option D is incorrect.
Logistic regression is not a valid option when using SageMaker hyperparameter tuning.
Reference:
Please see the Amazon SageMaker developer guide titled How Hyperparameter Tuning Works (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html), the AWS Machine Learning blog titled Amazon SageMaker automatic model tuning now supports random search and hyperparameter scaling (https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-automatic-model-tuning-now-supports-random-search-and-hyperparameter-scaling/), and the AWS Machine Learning blog titled Simplify machine learning with XGBoost and Amazon SageMaker (https://aws.amazon.com/blogs/machine-learning/simplify-machine-learning-with-xgboost-and-amazon-sagemaker/)
In the context of hyperparameter tuning, the aim is to identify the optimal set of hyperparameters that produce the best performing model. The challenge is that there are often many hyperparameters to tune, and a brute-force search of all possible combinations can be computationally expensive and time-consuming. Therefore, hyperparameter tuning techniques have been developed to help solve this problem more efficiently.
The four techniques mentioned in the question are:
A. Bayesian optimization B. Bayesian classification C. Random search D. Logistic regression.
A. Bayesian optimization: Bayesian optimization is a technique for optimizing an objective function that is expensive to evaluate. It works by constructing a probabilistic model of the objective function, which is then used to choose the next set of hyperparameters to evaluate. This approach can be particularly effective when the objective function is noisy, non-convex, or has a large number of dimensions. Bayesian optimization is often used in machine learning for hyperparameter tuning because it can be more efficient than grid search or random search.
B. Bayesian classification: Bayesian classification is a probabilistic approach to classification that is based on Bayes' theorem. It involves estimating the probability of a particular class given a set of features, and then choosing the class with the highest probability. This technique is often used for text classification, spam filtering, and other applications where there is a large amount of data.
C. Random search: Random search is a simple technique for hyperparameter tuning that involves randomly selecting hyperparameters from a predefined search space. This approach can be effective when the search space is not too large, but it may require a large number of iterations to find the optimal set of hyperparameters.
D. Logistic regression: Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. It is commonly used in machine learning for binary classification problems, where the goal is to predict a binary outcome (e.g., fraud or no fraud).
Based on the requirements stated in the question, the best-suited hyperparameter tuning technique is C. Random search. The question states that the aim is to run the least number of hyperparameter tuning training jobs as possible. Random search is a simple and efficient approach that randomly selects hyperparameters from a predefined search space. It does not require any assumptions about the distribution of the hyperparameters or the objective function, which makes it a versatile technique that can work well in many different scenarios. While Bayesian optimization may be more efficient in some cases, it can require more iterations to find the optimal set of hyperparameters, which may not be desirable in this case. Therefore, random search is the most appropriate choice for this scenario.