SL: Linear Regression

Question 1

Lib to split train and test data?

Answer 1

From sklearn.model_selection import train_test_split

Question 2

Lib for linear regression?

Answer 2

From sklearn.linear_model import LinearRegression

Question 3

Lib to check model performance?

Answer 3

From sklearn.metrics import mean_square_error

Question 4

Code to split data into train & test?

Answer 4

X_train,x_test,y_train,y_test = train_test_split(x,y, test_size=0.30, random_state=1)

Question 5

train data on LinearRegression()

Answer 5

regression_model = LinearRegression()
regression_model.fit(X_train, y_train)

Question 6

get score of LinearRegression model

Answer 6

regression_model.score(X_train, y_train)

Question 7

What is the most popular ML technique

Answer 7

Linear regression

Question 8

What does Linear regression identify?

Answer 8

It identifies straight line relationships between two variables

Question 9

Linear regression example scenarios?

Answer 9

Does heavy car have low mileage

Question 10

First step in identifying relationships between two variables using linear regression?

Answer 10

Draw a scatter plot between mpg & weight

Question 11

What is the relationship between mpg and weight

Answer 11

Negative relationships

Question 12

How does positive relation look like?

Answer 12

If you draw a line in scatter plot the line goes up wards is positive relation

Question 13

How does a negative relation look like?

Answer 13

If you draw a line between scatter plot the line will go down wards is called negative relation

Question 14

What is non linear regression?

Answer 14

If you have scatter plot where straight line can not be drawn is non linear regression or no relationship

Question 15

Can we relay visualization as our only technique to find relation between two variables?

Answer 15

No

Question 16

What is covariance?

Answer 16

It quantify the linear relationships between two variables

Question 17

Covariance formula

Answer 17

Question 18

Data visualization is sufficient to identify the relationship between two variables?

Answer 18

No that is only the first step

Question 19

Points on 1 & 3 quadrant contribute to

Answer 19

Positive relation

Question 20

Points on 2 & 4 quadrant contribute to

Answer 20

Negative relation

Question 21

Points on all quadrant contribute to

Answer 21

Non linear relation

Question 22

Non linear relation covariance number?

Answer 22

0

Question 23

Larger positive covariance number represent

Answer 23

Positive relation

Question 24

Larger negative covariance number represent

Answer 24

Negative relation

Question 25

Which technique is used to measure the association of two variables

Answer 25

Covariance

Question 26

Covariance value is largely affected by

Answer 26

Unit of measure

Question 27

Formula for covariance unit normalization

Answer 27

Question 28

Max covariance between two variables is

Answer 28

1

Question 29

Linear meaning?

Answer 29

Straight line

Question 30

Eucledian space?

Answer 30

Point on a graph of x and y axis

Question 31

what is Linear regression?

Answer 31

Linear regression is a statistical method used to establish a relationship between a dependent variable and one or more independent variables.

Question 32

what are the types of linear regression?

Answer 32

The two main types of linear regression are simple linear regression and multiple linear regression.

Question 33

What is the difference between simple linear regression and multiple linear regression?

Answer 33

Simple linear regression involves one independent variable and one dependent variable. Multiple linear regression involves two or more independent variables and one dependent variable.

Question 34

What is the formula for simple linear regression?

Answer 34

y = mx + b, where y is the dependent variable, x is the independent variable, m is the slope, and b is the y-intercept.

Question 35

What is the formula for multiple linear regression?

Answer 35

y = b0 + b1x1 + b2x2 + … + bnxn, where y is the dependent variable, x1, x2, …, xn are the independent variables, and b0, b1, b2, …, bn are the coefficients.

Question 36

How do you plot the regression line in a simple linear regression model?

Answer 36

You can plot the regression line in a simple linear regression model using the scatter and plot functions of the matplotlib library.

Question 37

What is the difference between L1 and L2 regularization?

Answer 37

L1 regularization adds a penalty term to the loss function that is proportional to the absolute value of the coefficients, while L2 regularization adds a penalty term that is proportional to the square of the coefficients.

Question 38

How do you perform L1 regularization in a linear regression model in Python?

Answer 38

You can perform L1 regularization in a linear regression model using the Lasso regression algorithm in scikit-learn.

Question 39

How do you perform L2 regularization in a linear regression model in Python?

Answer 39

You can perform L2 regularization in a linear regression model using the Ridge regression algorithm in scikit-learn

Question 40

What is cross-validation, and why is it important?

Answer 40

Cross-validation is a technique used to evaluate the performance of a model by partitioning the data into training and validation sets multiple times. It is important because it helps to reduce the risk of overfitting and provides a more accurate estimate of the model’s performance.

Question 41

How do you perform cross-validation in a linear regression model in Python?

Answer 41

You can perform cross-validation in a linear regression model using the cross_val_score function in scikit-learn

Question 42

How do you evaluate the performance of a linear regression model?

Answer 42

You can evaluate the performance of a linear regression model using metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), R-squared, and adjusted R-squared.

Question 43

How do you perform feature scaling in a linear regression model in Python?

Answer 43

You can perform feature scaling in a linear regression model using techniques such as standardization and normalization

Question 44

How do you perform feature selection in a linear regression model in Python?

Answer 44

You can perform feature selection in a linear regression model using techniques such as forward selection, backward elimination, and stepwise regression.

Question 45

How do you handle categorical variables in a linear regression model in Python?

Answer 45

You can handle categorical variables in a linear regression model using techniques such as one-hot encoding and ordinal encoding

Question 46

How do you handle missing values in a linear regression model in Python?

Answer 46

You can handle missing values in a linear regression model using techniques such as mean imputation, median imputation, and forward filling.

Question 47

How do you handle outliers in a linear regression model in Python?

Answer 47

You can handle outliers in a linear regression model using techniques such as removing outliers, transforming the data, and using robust regression methods

Question 48

what is Noise?

Answer 48

Noise is something which is not expected to repeat.

Question 49

what is Under fit?

Answer 49

Did not capture required info.

Question 50

what is Over fit?

Answer 50

Captured required info & captured noise as well