decision tree

程序代写 Machine Learning and Data Mining in Business

程序代写 CS代考 / , ,

Machine Learning and Data Mining in Business Lecture 9: Gradient Boosting Discipline of Business Analytics Copyright By PowCoder代写 加微信 powcoder Lecture 9: Gradient Boosting Learning objectives • Boosting. • Gradient boosting. • XGBoost, LightGBM and CatBoost. Lecture 9: Gradient Boosting 1. Boosting 2. Least squares boosting 3. Gradient boosting 4. Practical details 5. Explainable Boosting […]

程序代写 Machine Learning and Data Mining in Business Read More »

程序代写 PowerPoint Presentation

程序代写 CS代考 /

PowerPoint Presentation 2021SM2 Workshop Week 9 Exercise 1 Copyright By PowCoder代写 加微信 powcoder 1 – What is classification? What is regression? What is the difference between the two: Classification: Attempting to map from input variables (x) to discrete or categorical output variables (y) Regression: Attempting to map from input variables (x) to numerical or continuous

程序代写 PowerPoint Presentation Read More »

程序代写 ISO-8859-1′)

程序代写 CS代考 / ,

2021SM2-Week9 _lab Classification and Regression¶ Copyright By PowCoder代写 加微信 powcoder As an example dataset, we will use the pima indian diabetes dataset, which records measurements about several hundred patients and an indication of whether or not they tested positive for diabetes (the class label). The classification is therefore to predict whether a patient will test

程序代写 ISO-8859-1′) Read More »

程序代写 STAT340 Lecture 16: Tree-Based Methods’

程序代写 CS代考 /

title: ‘STAT340 Lecture 16: Tree-Based Methods’ author: ” and Wu” date: “Fall 2021” output: html_document Copyright By PowCoder代写 加微信 powcoder “`{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) require(ggplot2) require(ISLR) __Readings:__ ISLR Section 8.1 In our last lectures of the semester, we are going to return to prediction and classification problems and see a very different set

程序代写 STAT340 Lecture 16: Tree-Based Methods’ Read More »

CS代写 ISO-8859-1′)

程序代写 CS代考 / ,

2021SM2-Week9_Solution Classification and Regression¶ Copyright By PowCoder代写 加微信 powcoder As an example dataset, we will use the pima indian diabetes dataset, which records measurements about several hundred patients and an indication of whether or not they tested positive for diabetes (the class label). The classification is therefore to predict whether a patient will test positive

CS代写 ISO-8859-1′) Read More »

编程代写 STAT340 Lecture 17: Bagging and Random Forests’

程序代写 CS代考 / ,

title: ‘STAT340 Lecture 17: Bagging and Random Forests’ author: ” and Wu” date: “Fall 2021” output: html_document Copyright By PowCoder代写 加微信 powcoder “`{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) require(ggplot2) require(ISLR) require(MASS) require(randomForest) __Readings:__ ISLR Section 8.2 We closed our last lecture by pointing out a couple of difficulties with regression trees: 1. Decision trees can

编程代写 STAT340 Lecture 17: Bagging and Random Forests’ Read More »

CS代考 CPSC 425: Computer Vision

程序代写 CS代考 / ,

CPSC 425: Computer Vision Lecture 34: Review 1 Today’s “fun” Example: Colorful Image Colorization Copyright By PowCoder代写 加微信 powcoder Final Exam Details 2.5 hours Closed book, no calculators — Equations will be given Format similar to midterm exam — Part A: Multiple-part true/false — Part B: Short answer No coding questions How to study? —

CS代考 CPSC 425: Computer Vision Read More »

留学生代考 EBU7240 Computer Vision

程序代写 CS代考 / , ,

EBU7240 Computer Vision Detection1: Pedestrian detection Semester 1, 2021 Changjae Oh Copyright By PowCoder代写 加微信 powcoder Outline • Overview • Dalal-Triggs (pedestrian detection) ̶ Histogram of Oriented Gradients ̶ Learning with SVM Object Detection • Focus on object search: “Where is it?” • Build templates that differentiate object patch from background patch Object or Non-Object?

留学生代考 EBU7240 Computer Vision Read More »