data analysis with python coursera week 4 quiz answers
Practice Quiz: Linear Regression and Multiple Linear Regression
1. consider the following lines of code, what is the name of the column that contains the target values:
from sklearn.linear_model import LinearRegression
lm=LinearRegression()
X = df[['highway-mpg']]
Y = df['price']
lm.fit(X, Y)
Yhat=lm.predict(X)
- ‘price’
- ‘highway-mpg’
2. consider the following equation:
the variable y is?
- the target or dependent variable
- the intercept
- the predictor or independent variable
Practice Quiz: Model Evaluation using Visualization
Practice Quiz: Polynomial Regression and Pipelines
Practice Quiz: Measures for In-Sample Evaluation
5. Consider the following lines of code; what value does the variable out contain?
lm = LinearRegression()
lm.score(X,y)
X = df[['highway-mpg']]
Y = df['price']
lm.fit(X, Y)
out=lm.score(X,y)
- Mean Squared Error
- The Coefficient of Determination or R^2
Graded Quiz: Model Development
6. What does the following line of code do?
lm = LinearRegression()
- Fit a regression object lm
- Create a linear regression object
- Predict a value
7. What is the maximum value of R^2 that can be obtained?
- 10
- 0
- 1
8. If X is a dataframe with 100 rows and 5 columns, and y is the target with 100 samples, and assuming all the relevant libraries and data have been imported, and the following line of code has been executed:
LR = LinearRegression()
LR.fit(X, y)
yhat = LR.predict(X)
How many samples does yhat contain?
- 500
- 100
- 5
9. Which statement is true about Polynomial linear regression?
- Polynomial linear regression is not linear in any way
- Although the predictor variables of Polynomial linear regression are not linear the relationship between the parameters or coefficients is linear
- Polynomial linear regression uses linear Wavelets
10. Consider the following equation:
What is the parameter b_0 (b subscript 0)?
- The predictor or independent variable
- The target or dependent variable
- The intercept
- The slope