Erich Smith Coleman Platt

Slides:

Advertisements

Similar presentations

Algorithms and applications

Advertisements

Biostatistics-Lecture 4 More about hypothesis testing Ruibin Xi Peking University School of Mathematical Sciences.

Math 5364 Notes Chapter 4: Classification

SVM—Support Vector Machines

CSCI 347 / CS 4206: Data Mining Module 07: Implementations Topic 03: Linear Models.

Outline Data with gaps clustering on the basis of neuro-fuzzy Kohonen network Adaptive algorithm for probabilistic fuzzy clustering Adaptive probabilistic.

Announcements  Project proposal is due on 03/11  Three seminars this Friday (EB 3105) Dealing with Indefinite Representations in Pattern Recognition.

Basic Data Mining Techniques Chapter Decision Trees.

Support Vector Machines

Supervised Learning & Classification, part I Reading: DH&S, Ch 1.

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

Kun Yi Li, Young Scholar Student, Quincy High School

Machine Learning: Final Presentation James Dalphond James McCauley Andrew Wilkinson Phil Kovac Data Set: Yeast GOLD TEAM.

Results Comparison with existing approaches on benchmark datasets Evaluation on a uveal melanoma datasetEvaluation on the two-spiral dataset Evaluation.

Foundation of High-Dimensional Data Visualization

Tree-Based Methods (V&R 9.1) Demeke Kasaw, Andreas Nguyen, Mariana Alvaro STAT 6601 Project.

A Simple Method to Extract Fuzzy Rules by Measure of Fuzziness Jieh-Ren Chang Nai-Jian Wang.

By Fatine Bourkadi.  Introduction to C4.5  Training Set  Test set  Data Sets  results.

CISC 4631 Data Mining Lecture 03: Introduction to classification Linear classifier Theses slides are based on the slides by Tan, Steinbach and Kumar (textbook.

© Negnevitsky, Pearson Education, Will neural network work for my problem? Will neural network work for my problem? Character recognition neural.

Xuhua Xia Slide 1 MANOVA All statistical methods we have learned so far have only one continuous DV and one or more IVs which may be continuous or categorical.

LOGO Ensemble Learning Lecturer: Dr. Bo Yuan

ICS 178 Introduction Machine Learning & data Mining Instructor max Welling Lecture 6: Logistic Regression.

 Create a PowerPoint from template using R software R and ReporteRs package Isaac Newton1/4.

Today Ensemble Methods. Recap of the course. Classifier Fusion

Local Fisher Discriminant Analysis for Supervised Dimensionality Reduction Presented by Xianwang Wang Masashi Sugiyama.

IE 585 Competitive Network – Learning Vector Quantization & Counterpropagation.

Linear Discriminant Analysis and Its Variations Abu Minhajuddin CSE 8331 Department of Statistical Science Southern Methodist University April 27, 2002.

© Tan,Steinbach, Kumar Introduction to Data Mining 8/05/ Data Mining: Exploring Data Lecture Notes for Chapter 3 Introduction to Data Mining by Tan,

Apache Mahout Qiaodi Zhuang Xijing Zhang.

Random Forests Ujjwol Subedi. Introduction What is Random Tree? ◦ Is a tree constructed randomly from a set of possible trees having K random features.

Using Classification Trees to Decide News Popularity

Introduction to Weka ML Seminar for Rookies Byoung-Hee Kim Biointelligence Lab, Seoul National University.

BIOSTATISTICS Explorative data analysis. Box plot QQ plot Classification analysis Copyright ©2012, Joanna Szyda INTRODUCTION.

Neural networks – Hands on

1 Statistics & R, TiP, 2011/12 Multivariate Methods  Multivariate data  Data display  Principal component analysis Unsupervised learning technique 

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Chapter 3 Basic Data Mining Techniques Jason C. H. Chen, Ph.D. Professor of MIS School of Business.

Introduction to Classifiers Fujinaga. Bayes (optimal) Classifier (1) A priori probabilities: and Decision rule: given and decide if and probability of.

Tree and Forest Classification and Regression Tree Bagging of trees Boosting trees Random Forest.

In part from: Yizhou Sun 2008 An Introduction to WEKA Explorer.

Machine Learning Reading: Chapter Classification Learning Input: a set of attributes and values Output: discrete valued function Learning a continuous.

Machine Learning: Ensemble Methods

Gilad Lerman Math Department, UMN

Data Mining Introduction to Classification using Linear Classifiers

Exploring Data: Summary Statistics and Visualizations

JMP Discovery Summit 2016 Janet Alvarado

Table 1. Advantages and Disadvantages of Traditional DM/ML Methods

CSE 4705 Artificial Intelligence

Discriminant Analysis

CSE 711: DATA MINING Sargur N. Srihari Phone: , ext. 113.

Machine Learning Dr. Mohamed Farouk.

Basic machine learning background with Python scikit-learn

GENETIC PROGRAMMING BBB4003.

Cloud Analytics Platforms

Machine Learning Week 1.

Students: Meiling He Advisor: Prof. Brain Armstrong

Figure 1.1 Rules for the contact lens data.

Classifiers Fujinaga.

A Fast and Scalable Nearest Neighbor Based Classification

CS621: Artificial Intelligence Lecture 17: Feedforward network (lecture 16 was on Adaptive Hypermedia: Debraj, Kekin and Raunak) Pushpak Bhattacharyya.

Weka Free and Open Source ML Suite Ian Witten & Eibe Frank

Principal Component Analysis (PCA)

DataMining, Morgan Kaufmann, p Mining Lab. 김완섭 2004년 10월 27일

Classification and Prediction

Thresholds to use for pkmc se < 54 < ve < 59 < vi sl

Statistical Learning Dong Liu Dept. EEIS, USTC.

Classifiers Fujinaga.

Full IRIS corrected setosa versicol verginic

GENETIC PROGRAMMING BBB4003.

Data Mining CSCI 307 Spring, 2019

Presentation transcript:

Erich Smith Coleman Platt Iris Dataset Erich Smith Coleman Platt

Summary 150 total data points Introduced by statistician Ronald Fisher in 1936 Widely used in machine learning examples Three species of Iris flower: Iris-setosa Iris-versicolor Iris-virginica Four continuous attributes: Length & width of petals (cm) Length & width of sepals (cm) 150 total data points 50 from each species

Questions to Answer How to distinguish between the three species based on measurements of their petals and sepals Accurately classify species that have multiple crossover attributes

Challenges Clustering not a good candidate due to attribute crossover Iris-setosa is linearly separable, but the other two are not Converting original data to format compatible with algorithm Deciding best cut off between training and test data

Methods Classification algorithms such as decision tree perform well with this data set We use C4.5 C4.5 is easy to use and interpret, and accurate even when given very small training data set

Results

Results

Results

Results

Results

Results

Results Predictably, the program is more accurate when given bigger percentage of data set as training data However, still very accurate when given only 10 training cases, producing only 6.7% error rate in test data Error rate stays approximately < 10% until given 50% or more of the data as training data

Related Work Comparing Classification Methods by DerekElliot 2 methods: Linear Regression v.s. Random Forest Linear Regression was a better fit for the data by a small margin Random Forest was off because of cleanliness of the data Linear regression correctly predicts that our decision tree was based on the pedal size.

Disciussion The data mining methods we used were able to satisfy our questions More data needed, combine data classification methods Making data compatible with algorithm, not simple

References Compare classification methods:2016. https://www.kaggle.com/saywhat1/d/uciml/iris/compare- classification-methods/notebook.Accessed:4/27/2016 C4.5Tutorial:1992. http://www2.cs.uregina.ca/~dbd/cs831/notes/ml/dtrees/c4.5/tutor ial.html. Accessed:4/23/2016 Iris Data Set: 1988. http://archive.ics.uci.edu/ml/datasets/Iris. Accessed: 4/23/2016