How does the Mean Squared Error (MSE) penalize larger errors compared to smaller errors?
It squares the errors, giving more weight to larger deviations.
It uses a logarithmic scale to compress larger errors.
It takes the absolute value of the errors, ignoring the sign.
It doesn't; all errors are penalized equally.
Which method in pandas is used to read a CSV file containing the dataset for Linear Regression?
read_csv()
from_csv()
load()
loadtxt()
What does the linearity assumption in linear regression imply?
The data points are evenly distributed around the regression line.
The relationship between the dependent and independent variables can be best represented by a straight line.
The dependent variable must have a normal distribution.
The independent variables are unrelated to each other.
What does a pattern in the residual plot suggest?
The residuals are normally distributed.
The linear model is not a good fit for the data, and a non-linear model may be more appropriate.
The linear model is a good fit for the data.
There is no relationship between the independent and dependent variables.
What does a high R-squared value indicate?
The model is a perfect fit for the data.
A large proportion of the variance in the dependent variable is explained by the independent variables.
The independent variables are not correlated with the dependent variable.
The model is not a good fit for the data.
What does a residual represent in linear regression?
The predicted value of the dependent variable.
The difference between the actual and predicted values of the dependent variable.
The intercept of the regression line.
The slope of the regression line.
What is a potential drawback of using a purely automated feature selection technique (like forward selection or backward elimination) without careful consideration?
It can sometimes overlook features that might be important in combination with others.
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.
What function from scikit-learn is used to perform Linear Regression?
model_selection.train_test_split()
linear_model.LogisticRegression()
preprocessing.StandardScaler()
linear_model.LinearRegression()
Feature selection in linear regression primarily aims to:
Make the model more complex and harder to interpret
Improve model performance and generalization by focusing on the most relevant predictors
Increase the number of features used for prediction
Ensure that all features have a statistically significant p-value
If the coefficient of determination (R-squared) for a linear regression model is 0.8, what does this indicate?
The model is a poor fit for the data.
20% of the variation in the dependent variable is explained by the independent variable.
There is a weak relationship between the independent and dependent variables.
80% of the variation in the dependent variable is explained by the independent variable.