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Question 72 - Professional Machine Learning Engineer discussion

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You need to train a regression model based on a dataset containing 50,000 records that is stored in BigQuery. The data includes a total of 20 categorical and numerical features with a target variable that can include negative values. You need to minimize effort and training time while maximizing model performance. What approach should you take to train this regression model?

A.
Create a custom TensorFlow DNN model.
Answers
A.
Create a custom TensorFlow DNN model.
B.
Use BQML XGBoost regression to train the model
Answers
B.
Use BQML XGBoost regression to train the model
C.
Use AutoML Tables to train the model without early stopping.
Answers
C.
Use AutoML Tables to train the model without early stopping.
D.
Use AutoML Tables to train the model with RMSLE as the optimization objective
Answers
D.
Use AutoML Tables to train the model with RMSLE as the optimization objective
Suggested answer: D

Explanation:

AutoML Tables is a service that allows you to automatically build, analyze, and deploy machine learning models on tabular data. It is suitable for large-scale regression and classification problems, and it supports various optimization objectives, data splitting methods, and hyperparameter tuning algorithms. AutoML Tables can handle both categorical and numerical features, and it can also handle missing values and outliers. AutoML Tables is a good choice for this problem because it minimizes the effort and training time required to train a regression model, while maximizing the model performance.

RMSLE stands for Root Mean Squared Logarithmic Error, and it is a metric that measures the average difference between the logarithm of the predicted values and the logarithm of the actual values. RMSLE is useful for regression problems where the target variable can include negative values, and where large differences between small values are more important than large differences between large values. For example, RMSLE penalizes underestimating a value of 10 by 2 more than overestimating a value of 1000 by 20. RMSLE is a good optimization objective for this problem because it can handle negative values in the target variable, and it can reduce the impact of outliers and large errors.

For more information about AutoML Tables and RMSLE, see the following references:

AutoML Tables: end-to-end workflows on AI Platform Pipelines

Predict workload failures before they happen with AutoML Tables

How to Calculate RMSE in R

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asked 18/09/2024
Krishnan S Sridhar
32 questions
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