Practical Data Science using Python - Naive Bayes Probability Model - Introduction

The video tutorial introduces the Naive Bayes classifier, a simple and fast probabilistic model based on Bayes Theorem. It covers its characteristics, such as handling high-dimensional data and being used as a benchmark classifier. The tutorial explains the theorem's formula and assumptions, including feature independence. Applications like spam filtering, sentiment analysis, and disease detection are discussed. An example of employee attrition classification is provided, demonstrating the model's application in real-world scenarios.

What is a key characteristic of the Naive Bayes classifier?

• It is simple and fast.

• It is only used for regression problems.

• It requires extensive parameter tuning.

• It is highly complex and slow.

Which of the following is NOT a typical application of Naive Bayes?

• Disease detection

• Image recognition

• Sentiment analysis

• Spam filtering

What does the Bayes theorem help calculate in Naive Bayes classification?

• The prior probability of features

• The posterior probability of features

• The probability of a class given features

• The likelihood of features

In the context of Naive Bayes, what is a 'label'?

• A constant value

• A predictor variable

• A target variable

• A feature variable

What assumption does the Naive Bayes classifier make about features?

• Features are dependent on each other.

• Features are independent of each other.

• Features are correlated with the target variable.

• Features are irrelevant to the classification.

In the employee attrition example, which feature is NOT considered a predictor?

• Latest rating

• Salary drawn

• Employee ID

• Years of experience

What type of data does the Gaussian Naive Bayes classifier work with?

• Categorical data

• Gaussian distributed data

• Binary data

• Time series data

Which of the following is an example of Gaussian data?

• Employee salary data

• Text data

• Image data

• Binary data

What is the main goal of the Naive Bayes classifier?

• To predict continuous values

• To classify data into categories

• To cluster data points

• To reduce dimensionality

Which of the following is a limitation of the Naive Bayes classifier?

• It assumes feature independence.

• It requires extensive parameter tuning.

• It cannot handle high-dimensional data.

• It is too slow for large datasets.