Kurssisisältö

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.


              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:

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.

Tehtävä

Swipe to start coding

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.

Ratkaisu

Vaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista

Oliko kaikki selvää?

Kiitos palautteestasi!

Osio 4. Luku 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:

Tehtävä

Swipe to start coding

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.

Ratkaisu

Vaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista

Oliko kaikki selvää?

Kiitos palautteestasi!

Osio 4. Luku 1

Vaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista