Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
3→1→2→4
B
1→2→3→4
C
1→3→2→4
D
2→1→3→4
Understanding the Answer
Let's break down why this is correct
Answer
The first step is to define the structure of the predictor, deciding what kind of model you will use and what inputs it will take. Next you gather the data that will be used to train and evaluate the model. After that you pick the parameters that specify the exact shape of the predictor, such as weights in a linear model or filter coefficients. Finally, you optimize those parameters by minimizing empirical risk, adjusting them until the predictor best fits the data. For example, you might design a linear regression, collect a set of measurements, choose a slope and intercept, and then adjust those values to reduce the average error on the training set.
Detailed Explanation
First you need data to see what the model will learn. Other options are incorrect because It assumes you can design and set parameters before seeing any data; It puts the structure before data.
Key Concepts
Parametrized Predictors
Empirical Risk Minimization
Predictive Modeling
Topic
Parametrized Predictors
Difficulty
easy level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1In the context of parametrized predictors, which statement best describes the role of parameters in the predictive model?
mediumComputer-science
Practice
2
Question 2Arrange the following steps in the process of evaluating a loss function in empirical risk minimization: A) Compute the predicted values using the predictor function, B) Determine the actual output values, C) Calculate the loss by comparing predicted and actual values, D) Use the loss to update the model parameters.
easyComputer-science
Practice
3
Question 3Arrange the following steps in the correct order of the Empirical Risk Minimization process: A) Select a loss function, B) Optimize the parameters of the model, C) Evaluate the model's performance on validation data, D) Collect and prepare the dataset.
hardComputer-science
Practice
4
Question 4Arrange the following steps in the correct order for applying regularization in predictive modeling: A) Analyze the model's performance on training data, B) Choose a regularization technique, C) Evaluate the model on validation data, D) Train the model with regularization applied.
easyComputer-science
Practice
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