Practice Supervised Learning – Logistic Regression (7) - Chapter 7: Supervised Learning – Logistic Regression

Practice: Supervised Learning – Logistic Regression

Lesson 7Supervised Learning – Logistic Regression

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11 questions

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4 cards from this section. Quick, good the night before a test.
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5 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is the purpose of logistic regression?
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Think about what types of outcomes it is used for.
Question 2.
What value range does the sigmoid function produce?
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Consider how probabilities are expressed.
3Question 3.
What type of output does logistic regression produce?
- Continuous Values
- Probabilities
- Categorical Values
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Consider the range of the sigmoid function.
4Question 4.
Which of the following is used to visualize logistic regression?
- Confusion Matrix
- Sigmoid Curve
- Accuracy Score
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Think about how classification changes with different inputs.
Question 5.
You have a logistic regression model that has a confusion matrix with the values: TP = 15, TN = 20, FP = 5, FN = 10. Calculate the model's accuracy and explain its significance.
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Use the total number of true results over the total number of all cases.
Question 6.
Given a logistic regression model's output probability of 0.65 for a new test case, discuss what classification it will yield and the implications of changing the threshold to 0.7.
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Always consider how threshold adjustments impact the sensitivity and specificity.

7 more questions in this section. Enroll free to practise them all.