Conteúdo do Curso

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

Mean Absolute Error (MAE)

Residuals plot is a good way to see if our model is good but not the best. Let's look at the metrics that are commonly used to evaluate a regression model. If we go through all the residuals and analyze them, we will understand how much we were wrong in each prediction. But what if we have a very large dataset?

Analyzing each of the tens of thousands of residues is quite a marginal business. Therefore, in this case it is easier to take the sum of residuals and divide it by the number of objects in our sample. We will find the mean of the residuals over the sample. Moreover, if we get a very small value that is close to zero, this does not mean that our model is good, it can happen when the positive and negative residuals themselves are reduced to a very small number.

Here the sum of residuals divided by its number is 0, but it doesn’t mean that our model is perfect. Thus, it becomes evident that taking the deviation of predictions from the truth and averaging is not the best calculation metric performance of our model. We need to come up with something more complex. First, we require to get rid of the minus sign. This can be done using the module, that is, take the sum of the modules of the differences (thereby, our negative deviation becomes positive and there will no longer be such that the negative and positive deviation add up to zero) and divide by the number of objects, thereby obtaining the mean absolute error (MAE).


              1
            
MAE = abs(residuals).mean()

We can also use the method mean_squared_error() from scikit-learn metrics module to output the same result:


              12
            
from sklearn.metrics import mean_absolute_error
print(mean_absolute_error(Y_test, y_test_predicted))

Ideally, when our mean absolute error tends to zero, in this case, we get into all the true values by our predictions.

The general formula for this metric:

Tarefa

Let’s calculate the MAE using the data from the previous task. You should find it mathematically and also using built-in function.

[Line #33] Assign the module average of residuals to the variable MAE_math.
[Line #36] Import mean_absolute_error for calculating metrics.
[Line #37] Use the method mean_absolute_error() to find MAE and assign it to the variable MAE_func.
[Line #40] Print MAE_math and MAE_func using the function print only one time.

Tarefa

Let’s calculate the MAE using the data from the previous task. You should find it mathematically and also using built-in function.

[Line #33] Assign the module average of residuals to the variable MAE_math.
[Line #36] Import mean_absolute_error for calculating metrics.
[Line #37] Use the method mean_absolute_error() to find MAE and assign it to the variable MAE_func.
[Line #40] Print MAE_math and MAE_func using the function print only one time.

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Seção 4. Capítulo 2

Mean Absolute Error (MAE)


              1
            
MAE = abs(residuals).mean()

We can also use the method mean_squared_error() from scikit-learn metrics module to output the same result:


              12
            
from sklearn.metrics import mean_absolute_error
print(mean_absolute_error(Y_test, y_test_predicted))

Ideally, when our mean absolute error tends to zero, in this case, we get into all the true values by our predictions.

The general formula for this metric:

Tarefa

Let’s calculate the MAE using the data from the previous task. You should find it mathematically and also using built-in function.

[Line #33] Assign the module average of residuals to the variable MAE_math.
[Line #36] Import mean_absolute_error for calculating metrics.
[Line #37] Use the method mean_absolute_error() to find MAE and assign it to the variable MAE_func.
[Line #40] Print MAE_math and MAE_func using the function print only one time.

Tarefa

Let’s calculate the MAE using the data from the previous task. You should find it mathematically and also using built-in function.

[Line #33] Assign the module average of residuals to the variable MAE_math.
[Line #36] Import mean_absolute_error for calculating metrics.
[Line #37] Use the method mean_absolute_error() to find MAE and assign it to the variable MAE_func.
[Line #40] Print MAE_math and MAE_func using the function print only one time.

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Tudo estava claro?

Seção 4. Capítulo 2

Mean Absolute Error (MAE)


              1
            
MAE = abs(residuals).mean()

We can also use the method mean_squared_error() from scikit-learn metrics module to output the same result:


              12
            
from sklearn.metrics import mean_absolute_error
print(mean_absolute_error(Y_test, y_test_predicted))

Ideally, when our mean absolute error tends to zero, in this case, we get into all the true values by our predictions.

The general formula for this metric:

Tarefa

Let’s calculate the MAE using the data from the previous task. You should find it mathematically and also using built-in function.

[Line #33] Assign the module average of residuals to the variable MAE_math.
[Line #36] Import mean_absolute_error for calculating metrics.
[Line #37] Use the method mean_absolute_error() to find MAE and assign it to the variable MAE_func.
[Line #40] Print MAE_math and MAE_func using the function print only one time.

Tarefa

Let’s calculate the MAE using the data from the previous task. You should find it mathematically and also using built-in function.

[Line #33] Assign the module average of residuals to the variable MAE_math.
[Line #36] Import mean_absolute_error for calculating metrics.
[Line #37] Use the method mean_absolute_error() to find MAE and assign it to the variable MAE_func.
[Line #40] Print MAE_math and MAE_func using the function print only one time.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?


              1
            
MAE = abs(residuals).mean()

We can also use the method mean_squared_error() from scikit-learn metrics module to output the same result:


              12
            
from sklearn.metrics import mean_absolute_error
print(mean_absolute_error(Y_test, y_test_predicted))

Ideally, when our mean absolute error tends to zero, in this case, we get into all the true values by our predictions.

The general formula for this metric:

Tarefa

Let’s calculate the MAE using the data from the previous task. You should find it mathematically and also using built-in function.

[Line #33] Assign the module average of residuals to the variable MAE_math.
[Line #36] Import mean_absolute_error for calculating metrics.
[Line #37] Use the method mean_absolute_error() to find MAE and assign it to the variable MAE_func.
[Line #40] Print MAE_math and MAE_func using the function print only one time.

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Seção 4. Capítulo 2

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