User: What would select in the quiz below?
1.
Question 1
Which is an example of a classification task?


Based on a patient's blood pressure, determine how much blood pressure medication (a dosage measured in milligrams) the patient should be prescribed.



Based on a patient's age and blood pressure, determine how much blood pressure medication (measured in milligrams) the patient should be prescribed.



Based on the size of each tumor, determine if each tumor is malignant (cancerous) or not.


Status: [object Object]
1 point
2.
Question 2
Recall the sigmoid function is 
g
(
z
)
=
1
1
+
e
−
z
g(z)= 
1+e 
−z
 
1
​
 



If z is a large positive number, then:


g
(
z
)
g(z) will be near zero (0) 



g
(
z
)
g(z) is near one (1) 



g
(
z
)
g(z) will be near 0.5 



g
(
z
)
g(z) is near negative one (-1) 


Status: [object Object]
1 point
3.
Question 3

A cat photo classification model predicts 1 if it's a cat, and 0 if it's not a cat. For a particular photograph, the logistic regression model outputs 
g
(
z
)
g(z) (a number between 0 and 1). Which of these would be a reasonable criteria to decide whether to predict if it’s a cat?


Predict it is a cat if g(z) < 0.7 



Predict it is a cat if g(z) = 0.5 



Predict it is a cat if g(z) >= 0.5 



Predict it is a cat if g(z) < 0.5 


Status: [object Object]
1 point
4.
Question 4

True/False? No matter what features you use (including if you use polynomial features), the decision boundary learned by logistic regression will be a linear decision boundary. 


False



True 


Status: [object Object]
1 point

Assistant: Here are the answers to the quiz questions:

1. **Question 1**: Which is an example of a classification task?
   - **Answer**: Based on the size of each tumor, determine if each tumor is malignant (cancerous) or not.
   - Explanation: A classification task involves assigning items to discrete categories or classes. Determining whether a tumor is malignant or not involves two classes, making it a classification task.

2. **Question 2**: If z is a large positive number, then \( g(z) \) (where \( g(z) = \frac{1}{1+e^{-z}} \)) will:
   - **Answer**: \( g(z) \) is near one (1).
   - Explanation: For large positive values of z, \( e^{-z} \) approaches zero, making \( g(z) \) approach 1.

3. **Question 3**: A cat photo classification model outputs \( g(z) \). Which criteria should be used to predict if it’s a cat?
   - **Answer**: Predict it is a cat if \( g(z) >= 0.5 \).
   - Explanation: In binary classification, \( g(z) \), the output of the logistic function, is often compared to a threshold of 0.5. If \( g(z) >= 0.5 \), the model typically predicts the positive class (in this case, "cat").

4. **Question 4**: True/False: The decision boundary learned by logistic regression will always be a linear decision boundary, regardless of features used.
   - **Answer**: False.
   - Explanation: While logistic regression is linear in the parameters, using polynomial features or other transformations of the input features can result in a non-linear decision boundary in the original feature space.
User: Do the same for these
1.
Вопрос 1
Что из нижеперечисленного может решить проблему избыточной подгонки?


Примените регуляризацию



Выберите подмножество наиболее значимых характеристик.



Соберите больше тренировочных данных



Удалите случайный набор обучающих примеров


Статус: [object Object]
1 балл
2.
Вопрос 2
Вы подгоняете логистическую регрессию с полиномиальными признаками к набору данных, и Ваша модель выглядит следующим образом. 




Какой бы Вы сделали вывод? (Выберите один)


Модель имеет высокую дисперсию (overfit). Таким образом, добавление данных, вероятно, поможет



Модель имеет высокую погрешность (underfit). Таким образом, добавление данных само по себе вряд ли сильно поможет. 



Модель имеет высокую дисперсию (overfit). Таким образом, добавление данных само по себе вряд ли сильно поможет. 



Модель имеет высокую смещенность (underfit). Таким образом, добавление данных, вероятно, поможет


Статус: [object Object]
1 балл
3.
Вопрос 3

Предположим, у Вас есть регуляризованная модель линейной регрессии. Если Вы увеличите параметр регуляризации 
λ
λ, что, по Вашему мнению, произойдет с параметрами 
w
1
,
w
2
,
.
.
.
,
w
n
w 
1
​
 ,w 
2
​
 ,...,w 
n
​
 ?


Это позволит уменьшить размер параметров 
w
1
,
w
2
,
.
.
.
,
w
n
w 
1
​
 ,w 
2
​
 ,...,w 
n
​
 .



Это увеличит размер параметров 
w
1
,
w
2
,
.
.
.
,
w
n
w 
1
​
 ,w 
2
​
 ,...,w 
n
​
 


Статус: [object Object]
1 балл

Assistant: