学ぶ Residuals | Metrics to Evaluate the Model

セクション 4. 章 1

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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.

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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.

解答

実践的な練習のためにデスクトップに切り替える下記のオプションのいずれかを利用して、現在の場所から続行する

すべて明確でしたか？

フィードバックありがとうございます！

セクション 4. 章 1

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