SL: Classification Flashcards

1
Q

What does Classification Algo predict?

A

It can predict Categorical (Non Numeric Values)

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2
Q

What does Linear Regression Algo predict?

A

It can predict Numeric Values

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3
Q

Email is spam or not spam is best predicted by?

A

Classification Algo

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4
Q

Which algo can Predict Car price?

A

Linear Regression Algo

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5
Q

Which algo can Predict Movie Rating?

A

Linear Regression Algo

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6
Q

Which algo can Predict customer is going to default on the Loan or not.?

A

Classification Algo

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7
Q

Which algo can Predict probability of a person having diabetes?

A

Classification Algo

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8
Q

Logistic Regression comes under which algorithmic

A

Classification Algo

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9
Q

Decision Tree comes under which Algo?

A

Classification Algo

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10
Q

Which Lib is used for Logistic Regression.

A

Sklearn Lib

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11
Q

What is regression?

A

Regression refers to a statistical technique used to analyze the relationship between one or more independent variables and a dependent variable.

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12
Q

Linear Regression and Logistic Regression are both supervised learning algorithms.?

A

Yes

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13
Q

Linear Regression solve which problem?

A

Regression Problem

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14
Q

Logistic Regression solve which problem

A

Classification Problem

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15
Q

What is the functionality of Linear regression?

A

Estimate the dependent variable when there is a change in independent variable.

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16
Q

What is the functionality of Logistic egression?

A

Calculate the posibility of an event occuring.

17
Q

What would be the curve of linear regression?

A

Straight Line.

18
Q

What would be the curve of Logistic Regression?

A

S curve or Sigmoid function

19
Q

What is Sigmoid function

A

The sigmoid function is a mathematical function that maps any input value to a value between 0 and 1. It has an S-shaped curve and is often used in machine learning and artificial intelligence algorithms.

20
Q

what will be the output of Linear Regression?

A

Any Numeric value.

21
Q

What will be the output of Logistic regression?

A

Specific Numeric / Non-Numeric value.

22
Q

Predict GDP of a country is solved by which algo?

A

Linear Regression

23
Q

Predict Product price is solved by which algo?

A

Linear Regression

24
Q

Predict Team Score is solved by which algo?

A

Liner Regression

25
Q

Predict House value is solved by which algo?

A

Linear Regression

26
Q

Predict Credit card transaction is fraud solved by which algo?

A

Logistic regression

27
Q

Predict customer will take a loan is solved by which algo?

A

Logistic Regression

28
Q

What is the meaning of odds in statistics?

A

Likelihood of an event occurring

29
Q

what is Odds Ratio?

A

likelihood of that an event will occur divided by probability of event will not occur.

30
Q

Odds Ratio Formula?

A

P/1-P

31
Q

Odds Ratio Formula for two events?

A

Odds A/Odds B

32
Q

Coefficient of Logistic regression?

A

Log of Odds

33
Q

What is Coefficient of Logistic regression?

A

“Imagine you have a group of 100 children, and you want to know how many of them like chocolate cake. You ask each child if they like chocolate cake, and 60 of them say “yes” while 40 say “no”.

Now, let’s say you want to predict if a new child will like chocolate cake or not based on some other information you have about them, like their age or their favorite color. This is where logistic regression comes in.

Logistic regression uses the information you have about the children to calculate a number called the “log odds”. The log odds tell you how likely it is that a child will like chocolate cake based on the information you have about them.

34
Q

what is sigmoid function?

A

sigmoid function is a mathematical function that maps any input value to a value between 0 and 1.

35
Q

Sigmoid function curve looks like?

A

S shaped Curve

36
Q

When the input value is very small, the output value of Sigmoid function is close to ?

A

0

37
Q

When the input value is very High, the output value of Sigmoid function is close to ?

A

1

38
Q

where we use sigmoid function?

A

sigmoid function is often used in logistic regression to model the probability of a binary outcome