Suppose that there is a color column (feature) in a data frame with values in ("Blue", "Green", "Black","Red"). What is the best way to prepare this column for modeling?NormalizationReplace the values with numbers (1,2,3,4)O Label encodingOne-hot encoding After calculating Mean Square Error (MSE) and Mean Absolute Error (MAE) for a linear model, weobserve that the value of MSE is much larger than the value of MAE. What are the possible reasons?Both A and BC. The data is not linearO B. There are outliers in the datasetO A. MAE is in y unit but MSE is squared

In this question we have been given MCQ questions related to Machine Learning (ML) models and we…

Suppose that there is a color column (feature) in a data frame with values in ("Blue", "Green", "Black", "Red"). What is the best way to prepare this column for modeling? Normalization Replace the values with numbers (1,2,3,4) Label encoding One-hot encoding

Suppose that there is a color column (feature) in a data frame with values in ("Blue", "Green", "Black", "Red"). What is the best way to prepare this column for modeling? Normalization Replace the values with numbers (1,2,3,4) Label encoding One-hot encoding

Operations Research : Applications and Algorithms

4th Edition

ISBN:9780534380588

Author:Wayne L. Winston

Publisher:Wayne L. Winston

Chapter24: Forecasting Models

Section: Chapter Questions

Problem 10RP

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Theoretical Computer Science

Usually, computer science seems like a practical field that involves studying, building, and working closely with computers. However, a subclass of this discipline also centers on the mathematical factors of the same. This is called theoretical computer science, and its focal point differentiates it from other branches of computer science: the rigorous mathematical approach it takes. This discipline falls under the umbrella of discrete mathematics, which is the study of countable or discontinuous mathematical structures.

Question

Suppose that there is a color column (feature) in a data frame with values in ("Blue", "Green", "Black",
"Red"). What is the best way to prepare this column for modeling?
Normalization
Replace the values with numbers (1,2,3,4)
O Label encoding
One-hot encoding

After calculating Mean Square Error (MSE) and Mean Absolute Error (MAE) for a linear model, we
observe that the value of MSE is much larger than the value of MAE. What are the possible reasons?
Both A and B
C. The data is not linear
O B. There are outliers in the dataset
O A. MAE is in y unit but MSE is squared

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