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Question 1 of 8
To increase the number of features in the model
To make the model more complex
To enhance model performance by focusing on relevant features
To collect more data
Question 2 of 8
User ratings
Genres watched
Movie budget
Viewing time of day
Question 3 of 8
By adding more noise to the data
By including all available features
By removing irrelevant features that could lead to noise
By making the model more complex
Question 4 of 8
Which statement best describes how companies like Spotify use feature selection?
They use all available data without selection
They only use basic features like song titles
They select relevant features like beats per minute and genre to personalize recommendations
They randomly select features
Question 5 of 8
Which feature selection method would be most appropriate when dealing with a large dataset and limited computational resources?
Wrapper Methods
Filter Methods
Genetic Algorithms
PCA
Question 6 of 8
What is the key difference between Wrapper and Embedded methods?
Wrapper methods are faster
Embedded methods only work with neural networks
Embedded methods perform feature selection during model training
Wrapper methods don't evaluate feature combinations
Question 7 of 8
In what scenario would PCA be the most suitable feature selection method?
When feature interpretability is crucial
When dealing with highly correlated features and dimensionality reduction is needed
When working with categorical variables only
When computational speed is the only concern
Question 8 of 8
What is the main limitation of the SelectKBest method?
It's too computationally expensive
It only works with numerical features
It doesn't consider feature interactions
It requires manual parameter tuning