Feature Engineering Studio Special Session

Slides:

Advertisements

Similar presentations

Query Classification Using Asymmetrical Learning Zheng Zhu Birkbeck College, University of London.

Advertisements

Feature Engineering Studio January 21, Welcome to Feature Engineering Studio Design studio-style course teaching how to distill and engineer features.

Feature Engineering Studio Special Session October 23, 2013.

Special Topics in Educational Data Mining HUDK5199 Spring term, 2013 February 18, 2013.

Sophomore Slumpware Predicting Album Sales with Artificial Neural Networks Matthew Wirtala ECE 539.

Indian Statistical Institute Kolkata

Special Topics in Educational Data Mining HUDK5199 Spring term, 2013 March 7, 2013.

Classification of the aesthetic value of images based on histogram features By Xavier Clements & Tristan Penman Supervisors: Vic Ciesielski, Xiadong Li.

High Throughput Computing and Protein Structure Stephen E. Hamby.

How computers answer questions An introduction to machine learning Peter Barnum August 7, 2008.

ML ALGORITHMS. Algorithm Types Classification (supervised) Given -> A set of classified examples “instances” Produce -> A way of classifying new examples.

Text Classification Using Stochastic Keyword Generation Cong Li, Ji-Rong Wen and Hang Li Microsoft Research Asia August 22nd, 2003.

Feature Engineering Studio February 23, Let’s start by discussing the HW.

Classifiers, Part 3 Week 1, Video 5 Classification  There is something you want to predict (“the label”)  The thing you want to predict is categorical.

CS 5604 Spring 2015 Classification Xuewen Cui Rongrong Tao Ruide Zhang May 5th, 2015.

SVMLight SVMLight is an implementation of Support Vector Machine (SVM) in C. Download source from :

Attention Deficit Hyperactivity Disorder (ADHD) Student Classification Using Genetic Algorithm and Artificial Neural Network S. Yenaeng 1, S. Saelee 2.

Slide Image Retrieval: A Preliminary Study Guo Min Liew and Min-Yen Kan National University of Singapore Web IR / NLP Group (WING)

Comparing the Parallel Automatic Composition of Inductive Applications with Stacking Methods Hidenao Abe & Takahira Yamaguchi Shizuoka University, JAPAN.

Advanced Methods and Analysis for the Learning and Social Sciences PSY505 Spring term, 2012 February 13, 2012.

Hurieh Khalajzadeh Mohammad Mansouri Mohammad Teshnehlab

Feature Engineering Studio September 23, Welcome to Mucking Around Day.

Hospitalization Prediction From Health Care Claims Adithya Renduchintala, Benjamin Martin, & Lance Legel University of Colorado Boulder  Data Mining 

Data Mining Teaching experience at the FIB. What is Data Mining? A broad set of techniques and algorithms brought from machine learning and statistics.

Feature Engineering Studio September 9, Welcome to Problem Proposal Day Rules for Presenters Rules for the Rest of the Class.

The Perceptron. Perceptron Pattern Classification One of the purposes that neural networks are used for is pattern classification. Once the neural network.

Overview of Part I, CMSC5707 Advanced Topics in Artificial Intelligence KH Wong (6 weeks) Audio signal processing – Signals in time & frequency domains.

Machine learning system design Prioritizing what to work on

Today Ensemble Methods. Recap of the course. Classifier Fusion

–The shortest distance is the one that crosses at 90° the vector u Statistical Inference on correlation and regression.

Feature (Gene) Selection MethodsSample Classification Methods Gene filtering: Variance (SD/Mean) Principal Component Analysis Regression using variable.

DNA Microarray Data Analysis using Artificial Neural Network Models. by Venkatanand Venkatachalapathy (‘Venkat’) ECE/ CS/ ME 539 Course Project.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Virtual Private Network Pattern Classification Joe Madden Fall 2010 ECE/CS/ME 539.

***Classification Model*** Hosam Al-Samarraie, PhD. CITM-USM.

Feature Engineering Studio September 9, Welcome to Feature Engineering Studio Design studio-style course teaching how to distill and engineer features.

A Brief Introduction and Issues on the Classification Problem Jin Mao Postdoc, School of Information, University of Arizona Sept 18, 2015.

Identifying “Best Bet” Web Search Results by Mining Past User Behavior Author: Eugene Agichtein, Zijian Zheng (Microsoft Research) Source: KDD2006 Reporter:

Feature Engineering Studio Special Session September 25, 2013.

Miloš Kotlar 2012/115 Single Layer Perceptron Linear Classifier.

Information Processing by Neuronal Populations Chapter 6: Single-neuron and ensemble contributions to decoding simultaneously recoded spike trains Information.

Support Vector Machines Optimization objective Machine Learning.

Machine Learning Usman Roshan Dept. of Computer Science NJIT.

Course Outline (6 Weeks) for Professor K.H Wong

2/13/2018 4:38 AM © Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN.

Core Methods in Educational Data Mining

Machine Learning – Classification David Fenyő

6. Kernel Regression.

Core Methods in Educational Data Mining

Core Methods in Educational Data Mining

Introduction to Data Science Lecture 7 Machine Learning Overview

Schizophrenia Classification Using

NEURAL NETWORK APPROACHES FOR AUTOMOBILE MPG PREDICTION

Machine Learning Week 1.

Mitchell Kossoris, Catelyn Scholl, Zhi Zheng

חיזוי ואפיון אתרי קישור של חלבון לדנ"א מתוך הרצף

دانشگاه صنعتی امیرکبیر Instructor : Saeed Shiry & Bishop Ch. 1

Prediction of Wine Grade

Overview of Machine Learning

Machine Learning Course.

Lecture 6: Introduction to Machine Learning

Classification Boundaries

Intro to Machine Learning

Core Methods in Educational Data Mining

Kanchana Ihalagedara Rajitha Kithuldeniya Supun weerasekara

Analysis on Accelerated Learning Cohorts

MACHINE LEARNING IN ASIC BACKEND DESIGN

Trusting Machine Learning Algorithms for Safeguards Applications

What is Artificial Intelligence?

An introduction to Machine Learning (ML)

Presentation transcript:

Feature Engineering Studio Special Session February 25, 2015

RapidMiner 5.3 Data file and rapidminer xml file are on course webpage

Look at data

Look at process step-by-step

Build classifier

Goodness Criteria Kappa AUC (Warning!) Accuracy (Warning!) Precision Recall

Turn cross-validation off

Other types of cross-validation Student-level cross-validation Population-level cross-validation Content-level cross-validation When you use these….

Setting up other types of cross-validation BatchXValidation SetRole

CompleteFeatureGeneration

RemoveCorrelatedFeatures

Other Classification Algorithms W-J48 W-JRip W-KStar

Set up a Regression

Regression Algorithms Linear Regression W-RepTree W-M5P Neural Networks Support Vector Machines

Goodness Criteria Correlation (warning!) RMSE/MAD

Many other things RapidMiner can do… These are just two types of common prediction models

For a broader overview of prediction modeling… Come to April 1 special session

Questions? Concerns?