1.) We want to build a model to predict the weight (in Ibs) of a car. This prediction will be based onmultiple features of the car, such as: "Number of Cylinders", "Miles Per Gallon", "Production Year",etc.To train the model, we are given 1000 examples of cars along with the feature values and class foreach car.What technique could we use in this case?Multiple Linear RegressionSimple Linear RegressionO K-Means ClusteringK-Nearest Neighbors 4.) Suppose you are trying to tune a classification algorithm and you try several different hyper-parameter settings. These are the different results you obtain. Which one of them is likely to beoverfitting?Training Accuracy330% and Test Accuracy=32%O Training Accuracy=97% and Test Accuracy=77%Training Accuracy380% and Test Accuracy=75%

As this is a multiple type question, according to the guidelines only first question has been…

1.) We want to build a model to predict the weight (in Ibs) of a car. This prediction will be based on multiple features of the car, such as: "Number of Cylinders", "Miles Per Gallon", "Production Year", etc. To train the model, we are given 1000 examples of cars along with the feature values and class for each car. What technique could we use in this case? Multiple Linear Regression Simple Linear Regression K-Means Clustering K-Nearest Neighbors

1.) We want to build a model to predict the weight (in Ibs) of a car. This prediction will be based on multiple features of the car, such as: "Number of Cylinders", "Miles Per Gallon", "Production Year", etc. To train the model, we are given 1000 examples of cars along with the feature values and class for each car. What technique could we use in this case? Multiple Linear Regression Simple Linear Regression K-Means Clustering K-Nearest Neighbors

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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