Naive bayes Samenvattingen, Aantekeningen en Examens

Data Mining Exam 2 Study Guide X - correct answer-attribute, predictor, independent variable, input y - correct answer-class, response, dependent variable, output Classification - correct answer-predicts categorical labels Prediction - correct answer-predicts continuous values Decision Tree - correct answer-a non-parametric supervised learning algorithm, which is utilized for both classification and regression tasks. It has a hierarchical, tree structure, which consists of a root node, br...

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!

Data bricks Certified Data Engineer Professional Exam 1.A data engineer has written the following query: 1. SELECT * 2. FROM json.`/path/to/json/`; The data engineer asks a colleague for help to convert this query for use in a Delta Live Tables (DLT) pipeline. The query should create the first table in the DLT pipeline. Which of the following describes the change the colleague needs to make to the query? A. They need to add a CREATE LIVE TABLE table_name AS line at the begin...

Extensive, clear summary of the course 'Introduction to Machine Learning', based on elaborated notes made during the lecture, including graphs and pictures, connected to the lecture slides. The document is divided in 2 parts, the first part concerning the midterm material, the second part concerning the additional final exam material (everything is in this document).

In dit document staat de hele curus over AI Programming samengevat. Het gaat over algemene zaken zoals AI en machine learning tot specifieke zoekalgoritmen. Enkele behandelde onderwerpen: agents, POAS, zoekalgoritmes (local & adverserial), minimax, native bayes, machine learning, k-means clustering. Hopelijk helpt dit bij het studeren. Succes!

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...

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 ...

Machine Learning samenvatting voor 2e jaar artificial intelligence studie. Alle chapters en lectures voor het tentamen.