Random forest (Machine learning)
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This manuscript addresses the fundamentals of the random forest technique, which is a part of supervised machine learning.
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Random forest is an ensemble classifier that consists of many decision trees, where each tree depends on the values of a random vector sampled independently from the input data. It combines Breiman's "bagging" idea and the random selection of features to construct a set of decision trees with controlled variance. The random forest algorithm builds decision trees using randomly selected subsets of the training data and randomly selected subsets of input features. Each tree provides a class prediction and the class with the most votes becomes the random forest's prediction. Random forests have advantages including high accuracy, efficiency on large datasets, ability to handle thousands of variables, and estimates of feature importance.
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Random forest is a machine learning algorithm that combines multiple decision trees to improve predictive accuracy. It works by constructing many decision trees during training and outputting the class that is the mode of the classes of the individual trees. Random forest reduces overfitting and variance compared to a single decision tree. It can handle both classification and regression problems and provides flexibility and easy feature importance evaluation. However, it can be time-consuming and require more resources compared to a single decision tree model.
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Random forest is an ensemble learning technique that builds multiple decision trees and merges their predictions to improve accuracy. It works by constructing many decision trees during training, then outputting the class that is the mode of the classes of the individual trees. Random forest can handle both classification and regression problems. It performs well even with large, complex datasets and prevents overfitting. Some key advantages are that it is accurate, efficient even with large datasets, and handles missing data well.
Algoritma Random Forest beserta aplikasi nya
Random forest is an ensemble classifier that consists of many decision trees. It outputs the class that is the mode of the classes from individual trees. Each tree is constructed by selecting a random sample of training cases and a small random subset of input variables. Trees are fully grown and not pruned, and each tree votes for the most popular class. The random forest algorithm averages these votes for classification or averages predictions for regression. Random forests have advantages such as high accuracy, efficiency with large datasets, and estimates of variable importance.
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Random forest is an ensemble classifier that consists of many decision trees. It combines bagging and random selection of features to construct trees and outputs the class that is the mode of the classes from individual trees. Each tree is constructed using a bootstrap sample of training data and randomly selecting features at each node to split on. New samples are classified by pushing them through each tree and taking the average vote. Random forest provides accurate classification, handles thousands of variables, estimates variable importance, and generates unbiased error estimates as trees are constructed.
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Random forest is an ensemble classifier that consists of many decision trees, where each tree depends on the values of a random vector sampled independently from the input data. It combines Breiman's "bagging" idea and the random selection of features to construct a set of decision trees with controlled variance. The random forest algorithm builds decision trees using randomly selected subsets of the training data and randomly selected subsets of input features. Each tree provides a class prediction and the class with the most votes becomes the random forest's prediction. Random forests have advantages including high accuracy, efficiency on large datasets, ability to handle thousands of variables, and estimates of feature importance.
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Mechatronics material . Mechanical engineering
Mechatronics is a multidisciplinary field that refers to the skill sets needed in the contemporary, advanced automated manufacturing industry. At the intersection of mechanics, electronics, and computing, mechatronics specialists create simpler, smarter systems. Mechatronics is an essential foundation for the expected growth in automation and manufacturing.
Mechatronics deals with robotics, control systems, and electro-mechanical systems.
AI + Data Community Tour - Build the Next Generation of Apps with the Einstei...
AI + Data Community Tour - Build the Next Generation of Apps with the Einstei...Paris Salesforce Developer Group
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.一比一原版(uoft毕业证书)加拿大多伦多大学毕业证如何办理
原版一模一样【微信：741003700 】【(uoft毕业证书)加拿大多伦多大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(uoft毕业证书)加拿大多伦多大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(uoft毕业证书)加拿大多伦多大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(uoft毕业证书)加拿大多伦多大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(uoft毕业证书)加拿大多伦多大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Blood finder application project report (1).pdf
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
Supermarket Management System Project Report.pdf
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.
Open Channel Flow: fluid flow with a free surface
Open Channel Flow: This topic focuses on fluid flow with a free surface, such as in rivers, canals, and drainage ditches. Key concepts include the classification of flow types (steady vs. unsteady, uniform vs. non-uniform), hydraulic radius, flow resistance, Manning's equation, critical flow conditions, and energy and momentum principles. It also covers flow measurement techniques, gradually varied flow analysis, and the design of open channels. Understanding these principles is vital for effective water resource management and engineering applications.
Call For Paper -3rd International Conference on Artificial Intelligence Advan...
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Call for Research Papers!!!
Free – Extended Paper will be published as free of cost.
3rd International Conference on Artificial Intelligence Advances (AIAD 2024)
July 13 ~ 14, 2024, Virtual Conference
Webpage URL: https://aiad2024.org/index
Submission Deadline: June 22, 2024
Submission System URL:
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- 1. Random Forest Subject: Machine Learning Dr. Varun Kumar Subject: Machine LearningDr. Varun Kumar Lecture 8 1 / 13
- 2. Outlines 1 Introduction 2 Why Random Forest ? 3 What is Random Forest ? 4 Random Forest Example 5 How Random Forest Works ? 6 References Subject: Machine LearningDr. Varun Kumar Lecture 8 2 / 13
- 3. Introduction Supervised machine learning 1 Regression Linear regression Logistic regression 2 Classification: It is process for dividing a data sets into a different categories or groups by adding label. Decision tree Naive Bayes Random forest K nearest neighbor (KNN) Subject: Machine LearningDr. Varun Kumar Lecture 8 3 / 13
- 4. Random Forest ⇒ Random forest is an ensemble classifier made using decision tree models. ⇒ Ensemble model combines the results from different models. ⇒ Combination of multiple decision tree. Subject: Machine LearningDr. Varun Kumar Lecture 8 4 / 13
- 5. Continued– Subject: Machine LearningDr. Varun Kumar Lecture 8 5 / 13
- 6. Why random forest ? Use case- Credit risk detection Subject: Machine LearningDr. Varun Kumar Lecture 8 6 / 13
- 7. Continued– 1 To minimize the loss (Bank need a decision rule to predict for giving an approval to the loan.) 2 An applicant demographic (income, debit/credit history and socio-economic profiles are considered.) 3 Data science based assistance tool (Helps for modeling the behavioral patterns of individual customer) Variable Measurements Marital status Married or unmarried Gender Male or female Age Varried Status Default or not Time of payment Varried Employment Employed or un-employed Home ownership With home or without home Education level Secondary above or below Subject: Machine LearningDr. Varun Kumar Lecture 8 7 / 13
- 8. What is random forest ? ⇒ Random forest is versatile algorithm and capable with Regression Classification ⇒ It is a type of ensemble learning method. ⇒ Commonly used predictive modeling and machine learning techniques. Subject: Machine LearningDr. Varun Kumar Lecture 8 8 / 13
- 9. Random forest algorithm T: Number of features D: Number of trees to be constructed Subject: Machine LearningDr. Varun Kumar Lecture 8 9 / 13
- 10. How random forest works Days Outlook Humidity Wind Play 01 Sunny High Weak No 02 Sunny High Strong No 03 Overcast High Weak Yes 04 Rain High Weak Yes 05 Rain Normal Weak Yes 06 Rain Normal Strong No 07 Overcast Normal Strong Yes 08 Sunny High Weak No 09 Sunny Normal Weak Yes 10 Rain Normal Weak Yes 11 Sunny Normal Strong Yes 12 Overcast High Strong Yes 13 Overcast Normal Weak Yes 14 Rain High Strong ‘ No Subject: Machine LearningDr. Varun Kumar Lecture 8 10 / 13
- 11. Continued– Subject: Machine LearningDr. Varun Kumar Lecture 8 11 / 13
- 12. Features of random forest 1 Most accurate learning algorithm 2 Works well for both classification and regression problem. 3 Runs efficiently on large data base 4 Requires almost no input preparation 5 Performs implicit features selection 6 Can be easily grown in parallel 7 Methods for balancing error in unbalanced data set. Important steps ⇒ Data acquisition ⇒ Divide data set → (1) Training data set (2) Testing data set ⇒ Implement model ⇒ Visualize ⇒ Model validation Subject: Machine LearningDr. Varun Kumar Lecture 8 12 / 13
- 13. References E. Alpaydin, Introduction to machine learning. MIT press, 2020. T. M. Mitchell, The discipline of machine learning. Carnegie Mellon University, School of Computer Science, Machine Learning , 2006, vol. 9. J. Grus, Data science from scratch: first principles with python. O’Reilly Media, 2019. Subject: Machine LearningDr. Varun Kumar Lecture 8 13 / 13