What is a potential drawback of using a purely automated feature selection technique (like forward selection or backward elimination) without careful consideration?
It guarantees the most interpretable model.
It completely eliminates the need for domain expertise in model building.
It can lead to models that are less accurate than using all available features.
It can sometimes overlook features that might be important in combination with others.
Which of the following indicates a strong positive correlation between two variables?
Correlation coefficient (r) close to 1
A p-value greater than 0.05
Correlation coefficient (r) close to 0
Correlation coefficient (r) close to -1
Which of these methods can be used to address heteroscedasticity?
All of the above
Transforming the dependent variable
Removing outliers
Adding more independent variables
Which Python library is primarily used for numerical computing and provides support for arrays and matrices, essential for Linear Regression calculations?
NumPy
pandas
scikit-learn
matplotlib
Which of the following is NOT a benefit of feature selection in linear regression?
Improved model interpretability
Increased risk of overfitting
Reduced computational cost
Potential for better generalization to new data
Which of the following is the general equation for a simple linear regression model?
y = b0 + b1*x + e
y = b0 + b1x1 + b2x2 + ... + bn*xn
y = b0 * x^b1
y = e^(b0 + b1*x)
Which of the following situations might make feature selection particularly important?
Having a very large dataset with only a few features
When all features are highly correlated with the target variable
When computational resources are unlimited
Having a small dataset with a very large number of features
What does the 'fit_intercept' parameter in 'LinearRegression()' control?
Whether to calculate the intercept (bias) of the line.
Whether to use gradient descent for optimization.
Whether to normalize the data before fitting.
Whether to calculate the slope of the line.
What type of visualization tool is commonly used to initially assess the relationship between two continuous variables in linear regression?
Bar chart
Scatter plot
Pie chart
Histogram
What is the purpose of the coefficient of determination (R-squared) in linear regression?
To measure the proportion of variation in the dependent variable explained by the independent variable(s).
To determine the statistical significance of the model.
To identify the presence of outliers in the data.
To assess the linearity assumption of the model.