Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
Matrix Multiplication
B
Polynomial Expansion
C
Logistic Regression
D
Data Normalization
Understanding the Answer
Let's break down why this is correct
Answer
The operation that is most important in linear algebra for machine‑learning data is matrix multiplication. It lets you combine input vectors with weight matrices to produce predictions, and it can be used to transform, rotate, or scale the data. By multiplying a data matrix by a weight matrix you obtain a new feature set or a prediction vector. For example, if you have a 3×2 matrix of three samples with two features each, multiplying it by a 2×1 weight vector turns each sample into a single output value. This simple operation underlies most linear models and deep‑learning layers.
Detailed Explanation
Matrix multiplication lets you combine rows and columns of numbers to create new data. Other options are incorrect because People think adding more features automatically improves models, but that is feature engineering, not a linear algebra operation; Logistic regression is a statistical method that uses linear algebra inside, but it is not an operation itself.
Key Concepts
Matrix Operations
Machine Learning Algorithms
Data Manipulation
Topic
Linear Algebra in Machine Learning
Difficulty
easy level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1In machine learning, how does matrix multiplication facilitate the computation of predictions in a linear regression model?
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2
Question 2In machine learning, the performance of a model often improves with the optimization of its parameters through linear algebra techniques. What is the underlying reason why matrix operations are so critical in this optimization process?
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3
Question 3How does linear algebra facilitate the optimization processes in machine learning models?
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4
Question 4Which of the following statements about the role of linear algebra in machine learning are true? Select all that apply.
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5
Question 5Order the following steps in the process of applying linear algebra in machine learning: A) Model training, B) Data preparation, C) Matrix operations, D) Model evaluation.
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