Contents

- 1 What is conditional probability in machine learning?
- 2 What is supervised learning what are the preferable conditions to use the supervised learning?
- 3 Which of the learning methodology applies conditional probability?
- 4 What is supervised learning when it should be used explain?
- 5 What is conditional probability explain with an example?
- 6 Where is conditional probability used in real life?
- 7 Which model helps SVM to implement the algorithm in high dimensional space?
- 8 What is the standard approach to supervised learning?
- 9 What do you think is the standard approach to supervised learning?
- 10 Is conditional probability the same as dependent?

## What is conditional probability in machine learning?

In machine learning notation, the conditional probability distribution of Y given X is the probability distribution of Y if X is known to be a particular value or a proven function of another parameter. Both can also be categorical variables, in which case a probability table is used to show distribution.

## What is supervised learning what are the preferable conditions to use the supervised learning?

In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.

## Which of the learning methodology applies conditional probability?

The learning methodology that applies conditional probability of all the variables with respective the dependent variable and generally conditional probability of variables is nothing but a basic method of estimating the statistics for few random experiments.

## What is supervised learning when it should be used explain?

Supervised learning, also known as supervised machine learning, is a subcategory of machine learning and artificial intelligence. It is defined by its use of labeled datasets to train algorithms that to classify data or predict outcomes accurately.

## What is conditional probability explain with an example?

Conditional probability: p(A|B) is the probability of event A occurring, given that event B occurs. Example: given that you drew a red card, what’s the probability that it’s a four (p(four|red))=2/26=1/13. So out of the 26 red cards (given a red card), there are two fours so 2/26=1/13.

## Where is conditional probability used in real life?

Let’s take a real-life example. Probability of selling a TV on a given normal day maybe only 30%. But if we consider that given day is Diwali, then there are much more chances of selling a TV. The conditional Probability of selling a TV on a day given that Day is Diwali might be 70%.

## Which model helps SVM to implement the algorithm in high dimensional space?

-A set of algorithms called ‘Kernel methods’ are used to implement non-linear classification. -Kernel trick is helpful to do pattern analysis by mapping inputs in higher dimensional space.

## What is the standard approach to supervised learning?

The standard approach to supervised learning is to split the set of example into the training set and the test. 11) What is ‘Training set’ and ‘Test set’? In various areas of information science like machine learning, a set of data is used to discover the potentially predictive relationship known as ‘Training Set’.

## What do you think is the standard approach to supervised learning?

10) What is the standard approach to supervised learning? The standard approach to supervised learning is to split the set of example into the training set and the test.

## Is conditional probability the same as dependent?

Conditional probability is probability of a second event given a first event has already occurred. A dependent event is when one event influences the outcome of another event in a probability scenario.