APPLICATION OF SUPERVISED LEARNING
MACHINE LEARNING PIPELINE
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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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?
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Standard scaler
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Normalizer
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Max absolute scaler
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One hot encoder
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Explanation:
Detailed explanation-1: -Data Ingestion Each ML pipeline starts with the Data ingestion step. In this step, the data is processed into a well-organized format, which could be suitable to apply for further steps.
Detailed explanation-2: -A machine learning pipeline is the end-to-end construct that orchestrates the flow of data into, and output from, a machine learning model (or set of multiple models). It includes raw data input, features, outputs, the machine learning model and model parameters, and prediction outputs.
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