---
# System prepended metadata

title: 3. Naive Bayes Estimator Lab_3

---

# 3. Naive Bayes Estimator Lab_3

We are using this book as the reference for the NB algorithm.
https://jakevdp.github.io/PythonDataScienceHandbook/

The codes are available here:
https://colab.research.google.com/drive/1PpETS5qViMk8__Mgid-DBTkfzK8DecKv

The details of the algorithm is here https://scikit-learn.org/stable/modules/naive_bayes.html#naive-bayes

## What is this Bayes... 

**Danyar**
Bayes's Theorem is a way to figure out conditional odds, or the chance that one event will happen if another event has already happened.

**Moric**
the conditional probability of an event, based on the occurrence of another event, is equal to the likelihood of the second event given the first event multiplied by the probability of the first event.

**M Nuaimi**

The theory helps figure out how likely it is that a certain event will happen, given that a statistically similar event has already happened.

The theory calculates the likelihood of one event occurring if another statistically related one occurred.

**Azlan**
Naive Bayes algorithm is a supervised learning algorithm, which is based on Bayes theorem and used for solving classification problems.

**M Umar**
1. Bayes deals with fundamental concepts in probability theory/statistics. It gives a way to update the probability of an event based on new information or evidence. it can be use to calculate the probablity of an event X to likelihood to occur.

## Why the algorithm is called naive?
**Umar**
It is called naive because, it ignores prior distribution of parameters and assume independence of all features and all rows.

**Azlan**
because of its simplistic and "naive" assumption of independence among the features. The term "naive" is used to convey the simplicity of the algorithm's assumption rather than implying any inherent flaw or lack of intelligence in the approach.

**Danyar**
it is called naive because it sees the features as an independant feature meaning that, the existing of one feature has no thing to dod with the existing of another featur 

**Moric**
Because it assumes that each input variable is independent. This is a strong assumption, unrealistic for real data.the technique is very effective on a large range of complex problems.

**M Nuaimi**
the "naive conditional independence assumption." This assumption simplifies the computation of probabilities in Bayes' theorem and makes the algorithm computationally efficient.

naive assumption of conditional independence. This assumption makes it easier to figure out the odds in Bayes' theorem, which makes the method easy to use.

## Describe the dataset fetch_20newsgroups



## 