Зміст курсу

Explore the Linear Regression Using Python

1. What is the Linear Regression?

Basic Understanding of the Linear Regression How Mathematically Does It Work?Build the Linear Regression Working with Dataset Useful Functions for Researching and Visualizing Dataset

2. Correlation

What Is Correlation? How To Find Pearson Coefficient Correlation Matrix Calculating the Pearson Coefficient Using NumPy and Pandas Challenge

3. Building and Training Model

Train-split evaluation Fitting Prediction Challenge

4. Metrics to Evaluate the Model

Residuals Mean Absolute Error (MAE)MSE and RMSE R-squared Not Always the Linear Regression?

5. Multivariate Linear Regression

Building the Regression Model Based on Several Variables Try to Evaluate Challenge

Residuals

If we look at the plot that shows the dependence of flavanoids on the number of phenols, it will be obvious that the use of linear regression, in this case, was not entirely correct. Moreover, how do we interpret how good our prediction is?

Some points will lie on our constructed line, and some will lie away from it. We can measure the distance between a point and a line along the y-axis. This distance is called the residual or error. The remainder is the difference between the observed value of the target and the predicted value. The closer the residual is to 0, the better our model performs. Let's calculate the residuals and present them as a chart.


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residuals = Y_test - y_test_predicted

# Visualize the data 
ax = plt.gca() 
ax.set_xlabel('total_phenols') 
ax.set_ylabel('residuals') 
plt.scatter(X_test, residuals) 
plt.show()

Output:

Our residuals formed three almost straight lines. This distribution is a sign that the model is not working. Ideally, the remains should be arranged symmetrically and randomly around the horizontal axis. Still, if the residual graph shows some pattern (linear or non-linear), it means that our model is not the best.

Завдання

Try to find residuals to our previous challenge:

[Line #29] Define the variable y_test_predicted as predicted data for X_test.
[Line #30] Assign the difference between variables Y_test and y_test_predicted to the residuals.
[Line #31] Print the variable residuals.

Завдання

Try to find residuals to our previous challenge:

[Line #29] Define the variable y_test_predicted as predicted data for X_test.
[Line #30] Assign the difference between variables Y_test and y_test_predicted to the residuals.
[Line #31] Print the variable residuals.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

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Секція 4. Розділ 1

Residuals


              12345678
            
residuals = Y_test - y_test_predicted

# Visualize the data 
ax = plt.gca() 
ax.set_xlabel('total_phenols') 
ax.set_ylabel('residuals') 
plt.scatter(X_test, residuals) 
plt.show()

Output:

Завдання

Try to find residuals to our previous challenge:

[Line #29] Define the variable y_test_predicted as predicted data for X_test.
[Line #30] Assign the difference between variables Y_test and y_test_predicted to the residuals.
[Line #31] Print the variable residuals.

Завдання

Try to find residuals to our previous challenge:

[Line #29] Define the variable y_test_predicted as predicted data for X_test.
[Line #30] Assign the difference between variables Y_test and y_test_predicted to the residuals.
[Line #31] Print the variable residuals.

Все було зрозуміло?

Секція 4. Розділ 1

Residuals


              12345678
            
residuals = Y_test - y_test_predicted

# Visualize the data 
ax = plt.gca() 
ax.set_xlabel('total_phenols') 
ax.set_ylabel('residuals') 
plt.scatter(X_test, residuals) 
plt.show()

Output:

Завдання

Try to find residuals to our previous challenge:

[Line #29] Define the variable y_test_predicted as predicted data for X_test.
[Line #30] Assign the difference between variables Y_test and y_test_predicted to the residuals.
[Line #31] Print the variable residuals.

Завдання

Try to find residuals to our previous challenge:

[Line #29] Define the variable y_test_predicted as predicted data for X_test.
[Line #30] Assign the difference between variables Y_test and y_test_predicted to the residuals.
[Line #31] Print the variable residuals.

Все було зрозуміло?


              12345678
            
residuals = Y_test - y_test_predicted

# Visualize the data 
ax = plt.gca() 
ax.set_xlabel('total_phenols') 
ax.set_ylabel('residuals') 
plt.scatter(X_test, residuals) 
plt.show()

Output:

Завдання

Try to find residuals to our previous challenge:

[Line #29] Define the variable y_test_predicted as predicted data for X_test.
[Line #30] Assign the difference between variables Y_test and y_test_predicted to the residuals.
[Line #31] Print the variable residuals.

Секція 4. Розділ 1