MCQ IN COMPUTER SCIENCE & ENGINEERING

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COMPUTER SCIENCE AND ENGINEERING

MACHINE LEARNING

Question [CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
A real estate company is building a linear regression model to predict housing prices for different cities in the US. Which of the following is NOT a good metric to measure performance of their regression model?
A
R-Squared value
B
F1 score
C
Mean-squared error
D
Mean absolute error
Explanation: 

Detailed explanation-1: -Accuracy, confusion matrix, log-loss, and AUC-ROC are some of the most popular metrics. Precision-recall is a widely used metrics for classification problems.

Detailed explanation-2: -Step 2: Data Cleaning Next, this data flows to the cleaning step. To make sure the data paints a consistent picture that your pipeline can learn from, Cortex automatically detects and scrubs away outliers, missing values, duplicates, and other errors.

Detailed explanation-3: -Machine Learning algorithm to be used purely depends on the type of data in a given dataset.

Detailed explanation-4: -What Amazon SageMaker option should the company use to train their ML models that reduces the management and automates the pipeline for future retraining? Create and train your XGBoost algorithm on your local laptop and then use an Amazon SageMaker endpoint to host the ML model.

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