Order 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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Data preparation → B. Matrix operations → C. Model training → D. Model evaluation

Matrix operations → A. Model training → D. Model evaluation → C. Data preparation

Model evaluation → A. Model training → B. Matrix operations → D. Data preparation

Data preparation → C. Model evaluation → A. Model training → B. Matrix operations

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Answer

The first step is data preparation, where you clean, normalize, and format the data so it can be used by the algorithm. Next, you perform matrix operations, such as multiplying feature matrices by weight matrices, which is the core of linear algebra in the model. After that, you train the model, adjusting the weights based on the results of those matrix computations. Finally, you evaluate the trained model on unseen data to assess its performance. For example, you might take a dataset, convert it into a matrix, multiply it by a weight matrix, train the weights, and then test the accuracy on a validation set.

Detailed Explanation

First, you clean and organize the data so the numbers are ready. Other options are incorrect because This option starts with matrix operations before the data is cleaned, which can mix wrong or missing numbers; Evaluating before training assumes the model already knows how to work.

Key Concepts

Linear Algebra

Machine Learning

Optimization

Topic

Linear Algebra in Machine Learning

Difficulty

easy level question

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understand

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