What is the primary objective of linear regression in machine learning? A) Maximizing accuracy of predictions. B) Minimizing the number of features. C) Minimizing the sum of squared differences between predicted and actual values. D) Maximizing the correlation coefficient between variables.

What is the primary objective of linear regression in machine learning? A) Maximizing accuracy of predictions. B) Minimizing the number of features. C) Minimizing the sum of squared differences between predicted and actual values. D) Maximizing the correlation coefficient between variables.

Related questions

Q: Contrast the Bayes and the Frequentist approach to statistics.

A: Various scientific literature databases (such as ScienceDirect and Web of Science) indicate that an…

Q: probabilistic

A: Data matching is an important element of data analysis. It involves comparing multiple datasets in…

Q: You have four various machine learning algorithms to pick from for supervised learning on a dataset.…

A: The answer may be found below here: Please describe any four criteria that you would use to assist…

Q: (a) What is Gradient Descent and Optimizer in Machine Learning?

A: “Since you have asked multiple questions, we will solve the first question for you. If you want any…

Q: Match each the following example datasets (X,y) on the left to the most logical type of supervised…

A: Multivariate Linear Regression: Multiple independent variables contributing to the dependent…

Q: You have four various machine learning algorithms to pick from for supervised learning on a dataset.…

A: INTRODUCTION Give any four criteria you would use to decide which one to use for a given…

Q: Based on the understanding you have obtained about numerical estimation methods, critically evaluate…

A: The critical analysis examines the strengths, limitations, and real-life applications of numerical…

Q: Is it possible to explain the distinction between descriptive and inferential statistics?

A: Introduction: The properties of a data collection are summarized using descriptive statistics.…

Q: Justify your preferred machine learning model's use in a given scenario. There are two primary ways…

A: Before diving into whether it could be appropriate to use a certain machine learning Model, Note…

Q: You have four various machine learning algorithms to pick from for supervised learning on a dataset.…

A: Describe any four factors you would use to choose the solution to utilise for a specific project.

Q: Hours Grade 2) A teacher is interested to work out how time spent writing a logbook affects the…

Q: You have four various machine learning algorithms to pick from for supervised learning on a dataset.…

A: The answer may be found below here: Please describe any four criteria that you would use to assist…

Q: ne Learning question : a) Elaborate what is the meaning of hyperparameter tuning and why it is…

A: Q1 A model hyperparameter is a configuration that is external to the model and whose value cannot…

Q: 3. Answer the following questions about supervised learning: (a) What is the difference between…

A: Given: What are the distinctions between parametric and nonparametric models?

Q: 3. Answer the following questions about supervised learning: (b) What is the purpose of k-fold…

A: a) Purpose of k-fold cross validation: The k-fold cross validation procedure is used to estimate the…

Q: You have four various machine learning algorithms to pick from for supervised learning on a dataset.…

A: The answer of the question is given below

Q: 1. Suppose that a set of samples x1, x2, ..., xn, all real numbers, are drawn i.i.d. from the same…

A: Note: Answering the first question as per the guidelines. Input : Set of samples: x1,x2, ...., xn…

Q: An optimum solution only applies to a mathematical model that represents the issue

A: Optimizing solutions is a common practice in various fields, from engineering and finance to…

Q: 1. What is the difference between supervised learning and unsupervised learn- ing? 2. Given a task…

A: Well i am answering the first three question as per our guidelines 1) Supervised vs Unsupervised…

Q: A satisficing model will discover a decent, but not necessarily the best, solution to a problem.…

A: Here is the answer:

Q: How does parametric estimation differ from bottom-up estimation in software projects?

A: Estimation techniques play a role in software engineering by helping us assess the time, cost, and…

Q: What are some of the most commonly used algorithms in machine learning and how are they applied in…

A: There are numerous machine learning algorithms used in various applications, and selecting the best…

Concept explainers

System Model Approaches

The systems model approach is a process-oriented representation that emphasizes the impact or flow of information between modules. The multidisciplinary study of the use of models to design and create systems in business and IT development is known as systems model approach. It models how processes interact and what these processes do, but it does not go into detail about how these processes are implemented. Functional decomposition may be utilized to enhance the models employed in this technique, and they can be coupled to requirements models for additional system partitioning.

Question

What is the primary objective of linear regression in machine learning?

A) Maximizing accuracy of predictions.
B) Minimizing the number of features.
C) Minimizing the sum of squared differences between predicted and actual values.

D) Maximizing the correlation coefficient between variables.

Expert Solution

This question has been solved!

Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.

This is a popular solution!

SEE SOLUTION Check out a sample Q&A here

Step 1: Providing Introduction

VIEW

Step 2: Understanding the Primary Objective of Linear Regression in Machine Learning

VIEW

Solution

VIEW

Trending now

This is a popular solution!

Step by step

Solved in 3 steps

SEE SOLUTION Check out a sample Q&A here

Knowledge Booster

Learn more about

Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, ai-and-machine-learning and related others by exploring similar questions and additional content below.

Similar questions

The following is true about sensitivity: Group of answer choices a) The output of the model is said to be inversely sensitive if the output of the model changes a small amount for a large change in an input variable b) Sensitivity is not an important concept in modeling c) It can help the modeler tell, on a relative basis, what are the important variables d) A variable is considered NOT very sensitive if a small change in the variable results `in a large change in the output of the model.
(a) What is an Activation function in Machine Learning? (b) Discuss pros and cons of at least three Activation functions.
A model is used to generate a forecast using features as inputs and returning a prediction in a subset of machine learning called as is.... The following are some examples of new cutting-edge models that have become viable:
The distinction between descriptive and inferential statistics must be taught to the students.
Q. What can surrogate models enable? Select all that apply. A) They can help linearize strongly nonlinear forward models. B) They can help accelerate evaluation of the forward model or likelihood in Bayesian inference. C) They can make it easier to exploit parallel computing resources. D) They can reduce the dimensionality of the data. E) They can help push more computation to an "offline" phase of the analysis.
You have four various machine learning algorithms to pick from for supervised learning on a dataset. Describe any four factors you would use to help you decide which one to employ for a certain task.