Naive bayes - Study guides, Class notes & Summaries

This summary contains all the theory provided in the JBC090 course in 2021/2022. This includes elaborate description and practical examples of the concepts. This will help you preparing for the exam!

Naive Bayes Algorithm ppt

Provides sums on theory like Naive bayes theorem and Bayesian Belief networks

Unlock the world of Machine Learning with our comprehensive introduction notes! Dive into algorithms, data analysis, and AI concepts. Start your journey to mastering ML today ‍ Content in notes: Machine learning: Introduction, types of learning, application Supervised learning: Linear Regression Model, Naive Bayes classifier Decision Tree, K nearest neighbor, Logistic Regression, Support Vector Machine, Random forest algorithm

This document includes: a review of data mining, overfitting/underfitting, bias variance tradeoff, discrete random sampling, clustering, hierarchical methods, divisive method, dendrogram, Euclidean distance, k-means clustering, KNN, naive bayes, Bayes' Theorem, Model assessment, resampling, Leave one out cross validation approach, k-fold cross validation, stepwise selection, ridge regression, LASSO, regularized regression models in R, linear discrimination analysis, QDA, SVM, Logistic regressio...

In my iPad notes, I've created a concise overview of supervised learning algorithms, covering decision trees, CART, Naive Bayes classifiers, and Bayesian networks. It's basically a cheat sheet of the theory behind these algorithms. The best part is that I can easily take this summary with me anywhere to review.

Advanced Analytics: Theory and Methods (Naive Bayesian Classifier)

Year Major SubjectCode Unit Chapter Section QuestionType BTLevel COs DifficultyLevel Question Mark 2021 BIT 19ITEN2007 1 1 A Descriptive Remember CO1 Easy Define Machine Learning and List the real-life applications of ML algorithms 2 2021 BIT 19ITEN2007 1 1 A Descriptive Understanding CO1 Moderate Mention two methods by which we can replace NaN values from the Dataframe in Pandas. 2 2021 BIT 19ITEN2007 1 1 A Descriptive Understanding CO1 Easy Differentiate between supervised and unsupervised ...

Classification is a core supervised learning technique in data mining that assigns predefined labels to data points based on their features. The goal is to predict the category or class of new data points by learning from a labeled training dataset. Classification is widely used for tasks such as spam detection, medical diagnosis, and fraud detection. Purpose: The purpose of classification is to create models that can accurately predict the class or category of new, unseen data based on ...

The document is introduction to machine learning. It helps us to understand what is machine learning. Examples of machine learning various issues in machine learning the applications of machine learning. Then it emphasises on supervise learning and supervise learning. It emphasis on classification and regression and also it uses sums to make us understand various concepts like decision, trees, Naive bayes theorem Bayesian Belief model