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 startup wants to use ML to predict the value of homes in various cities. To do so, the startup’s data science team is joining real estate price data with other variables such as weather, demographic, and standard of living data. However, the team is having problems with slow model convergence. Additionally, the model includes large weights for some features, which is causing degradation in model performance. What kind of feature engineering technique should the team use to more effectively prepare this data and achieve a mean of 0 and standard deviation of 1?
A
Standard scaler
B
Normalizer
C
Max absolute scaler
D
One hot encoder
Explanation: 

Detailed explanation-1: -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-2: -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.

Detailed explanation-3: -To give inputs to a machine learning model, you have to create a NumPy array, where you have to input the values of the features you used to train your machine learning model. Then we can use that array in the model. predict() method, and at the end, it will give the predicted value as an output based on the inputs.

Detailed explanation-4: -Answer: (4) Classification Classification is a machine learning approach that helps address the question and the category to which the data belongs.

There is 1 question to complete.
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