
Linear Regression Flashcard
Flashcard
•
Information Technology (IT)
•
University
•
Practice Problem
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Hard
Wayground Content
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9 questions
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1.
FLASHCARD QUESTION
Front
Which of the following is least appropriate for linear regression?
Predicting house prices, Estimating someone's age from their Spotify playlist, Modelling the relationship between study time and exam scores, Generating random numbers
Back
Generating random numbers
Answer explanation
Generating random numbers is least appropriate for linear regression, as this technique is used for modeling relationships between variables, not for creating random data without a defined relationship.
2.
FLASHCARD QUESTION
Front
True or false: an R squared value of 0.85 means the model explains 85% of the variance in the dependent variable (the thing we are trying to predict)
Back
True
Answer explanation
True. An R squared value of 0.85 indicates that the model explains 85% of the variance in the dependent variable, confirming the statement is correct.
3.
FLASHCARD QUESTION
Front
What situation suggests your model is overfitting?
Back
It performs well on training data but poorly on new data
Answer explanation
The correct choice indicates overfitting when a model excels on training data but fails to generalize to new data, highlighting its inability to capture the underlying patterns effectively.
4.
FLASHCARD QUESTION
Front
What does the RMSE (Root Mean Squared Error) show?
Back
The average size of the prediction errors, in the same units as the target
Answer explanation
RMSE measures the average size of prediction errors, providing a clear indication of accuracy in the same units as the target variable. This makes it easier to interpret the model's performance.
5.
FLASHCARD QUESTION
Front
What is a residual in linear regression?
Back
The difference between the actual value and predicted value
Answer explanation
In linear regression, a residual is defined as the difference between the actual value and the predicted value. It measures how far off the predictions are from the true outcomes, making it a key concept in assessing model accuracy.
6.
FLASHCARD QUESTION
Front
What does it mean if the residuals are spread out randomly around zero?
Back
The model is doing a good job
Answer explanation
If the residuals are spread out randomly around zero, it indicates that the model is capturing the underlying data well without systematic errors, meaning the model is doing a good job.
7.
FLASHCARD QUESTION
Front
What is the purpose of the fit() method in scikit-learn?
Back
To build the regression line based on training data
Answer explanation
The fit() method in scikit-learn is used to build the regression line or model based on the training data provided. It adjusts the model parameters to best capture the underlying patterns in the data.
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