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Explore the Linear Regression Using Python
course content

Course Content

Explore the Linear Regression Using Python

Explore the Linear Regression Using Python

1. What is the Linear Regression?
Basic Understanding of the Linear RegressionHow Mathematically Does It Work?Build the Linear RegressionWorking with DatasetUseful Functions for Researching and Visualizing Dataset
2. Correlation
What Is Correlation? How To Find Pearson CoefficientCorrelation MatrixCalculating the Pearson Coefficient Using NumPy and PandasChallenge
3. Building and Training Model
Train-split evaluationFittingPredictionChallenge
4. Metrics to Evaluate the Model
ResidualsMean Absolute Error (MAE)MSE and RMSER-squaredNot Always the Linear Regression?
5. Multivariate Linear Regression
Building the Regression Model Based on Several VariablesTry to EvaluateChallenge

bookTry to Evaluate

Let’s see which model is better using the metrics we already know.

MSE:


              
123

            
from sklearn.metrics import mean_squared_error
print(mean_squared_error(Y_test, y_test_predicted).round(2))
print(mean_squared_error(Y_test, y_test_predicted2).round(2))
copy

MAE:


              
123

            
from sklearn.metrics import mean_absolute_error
print(mean_absolute_error(Y_test, y_test_predicted).round(2))
print(mean_absolute_error(Y_test, y_test_predicted2).round(2))
copy

R-squared:


              
123

            
from sklearn.metrics import r2_score
print(r2_score(Y_test, y_test_predicted).round(2))
print(r2_score(Y_test, y_test_predicted2).round(2))
copy

As a general rule, the more features a model includes, the lower the MSE (RMSE) and MAE will be. However, be careful about including too many features. Some of them may be extremely random, degrading the model's interpretability.

Task

Let’s evaluate the model from the previous task:

  1. [Line #30] Import mean_squared_error for calculating metrics from scikit.metrics.
  2. [Line #31] Find MSE using method mean_squared_error() and Y_test, y_test_predicted2 as the parameters, assign it to the variable MSE, round the result to second digit.
  3. [Line #32] Print the variable MSE.
  4. [Line #35] Import r2_score from scikit.metrics.
  5. [Line #36] Find R-squared and assign it to the variable r_squared, round the result to second digit.
  6. [Line #37] Print the variable r_squared.

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Section 5. Chapter 2
toggle bottom row

bookTry to Evaluate

Let’s see which model is better using the metrics we already know.

MSE:


              
123

            
from sklearn.metrics import mean_squared_error
print(mean_squared_error(Y_test, y_test_predicted).round(2))
print(mean_squared_error(Y_test, y_test_predicted2).round(2))
copy

MAE:


              
123

            
from sklearn.metrics import mean_absolute_error
print(mean_absolute_error(Y_test, y_test_predicted).round(2))
print(mean_absolute_error(Y_test, y_test_predicted2).round(2))
copy

R-squared:


              
123

            
from sklearn.metrics import r2_score
print(r2_score(Y_test, y_test_predicted).round(2))
print(r2_score(Y_test, y_test_predicted2).round(2))
copy

As a general rule, the more features a model includes, the lower the MSE (RMSE) and MAE will be. However, be careful about including too many features. Some of them may be extremely random, degrading the model's interpretability.

Task

Let’s evaluate the model from the previous task:

  1. [Line #30] Import mean_squared_error for calculating metrics from scikit.metrics.
  2. [Line #31] Find MSE using method mean_squared_error() and Y_test, y_test_predicted2 as the parameters, assign it to the variable MSE, round the result to second digit.
  3. [Line #32] Print the variable MSE.
  4. [Line #35] Import r2_score from scikit.metrics.
  5. [Line #36] Find R-squared and assign it to the variable r_squared, round the result to second digit.
  6. [Line #37] Print the variable r_squared.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 5. Chapter 2
toggle bottom row

bookTry to Evaluate

Let’s see which model is better using the metrics we already know.

MSE:


              
123

            
from sklearn.metrics import mean_squared_error
print(mean_squared_error(Y_test, y_test_predicted).round(2))
print(mean_squared_error(Y_test, y_test_predicted2).round(2))
copy

MAE:


              
123

            
from sklearn.metrics import mean_absolute_error
print(mean_absolute_error(Y_test, y_test_predicted).round(2))
print(mean_absolute_error(Y_test, y_test_predicted2).round(2))
copy

R-squared:


              
123

            
from sklearn.metrics import r2_score
print(r2_score(Y_test, y_test_predicted).round(2))
print(r2_score(Y_test, y_test_predicted2).round(2))
copy

As a general rule, the more features a model includes, the lower the MSE (RMSE) and MAE will be. However, be careful about including too many features. Some of them may be extremely random, degrading the model's interpretability.

Task

Let’s evaluate the model from the previous task:

  1. [Line #30] Import mean_squared_error for calculating metrics from scikit.metrics.
  2. [Line #31] Find MSE using method mean_squared_error() and Y_test, y_test_predicted2 as the parameters, assign it to the variable MSE, round the result to second digit.
  3. [Line #32] Print the variable MSE.
  4. [Line #35] Import r2_score from scikit.metrics.
  5. [Line #36] Find R-squared and assign it to the variable r_squared, round the result to second digit.
  6. [Line #37] Print the variable r_squared.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Let’s see which model is better using the metrics we already know.

MSE:


              
123

            
from sklearn.metrics import mean_squared_error
print(mean_squared_error(Y_test, y_test_predicted).round(2))
print(mean_squared_error(Y_test, y_test_predicted2).round(2))
copy

MAE:


              
123

            
from sklearn.metrics import mean_absolute_error
print(mean_absolute_error(Y_test, y_test_predicted).round(2))
print(mean_absolute_error(Y_test, y_test_predicted2).round(2))
copy

R-squared:


              
123

            
from sklearn.metrics import r2_score
print(r2_score(Y_test, y_test_predicted).round(2))
print(r2_score(Y_test, y_test_predicted2).round(2))
copy

As a general rule, the more features a model includes, the lower the MSE (RMSE) and MAE will be. However, be careful about including too many features. Some of them may be extremely random, degrading the model's interpretability.

Task

Let’s evaluate the model from the previous task:

  1. [Line #30] Import mean_squared_error for calculating metrics from scikit.metrics.
  2. [Line #31] Find MSE using method mean_squared_error() and Y_test, y_test_predicted2 as the parameters, assign it to the variable MSE, round the result to second digit.
  3. [Line #32] Print the variable MSE.
  4. [Line #35] Import r2_score from scikit.metrics.
  5. [Line #36] Find R-squared and assign it to the variable r_squared, round the result to second digit.
  6. [Line #37] Print the variable r_squared.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 5. Chapter 2
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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