ROC lift chart 2 Copyright (C) 2008 i-library MOT 2
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1 Data Mining --- Weka Weka MBA Weka Weka Weka Weka Labels & Links Weka Copyright (C) 2008 i-library MOT 1
2 ROC lift chart 2 Copyright (C) 2008 i-library MOT 2
3 1. August 16, 2007 Weka "Explorer" Copyright (C) 2008 i-library MOT 3
4 Copyright (C) 2008 i-library MOT 4
5 August 17, 2007 arff csv Copyright (C) 2008 i-library MOT 5
6 weather outlook {sunny, overcast, rainy} temperature PC TeraPad * Labels * Weka Copyright (C) 2008 i-library MOT 6
7 August 19, 2007 weather.arff csv csv weka "Labels" " " weka "Explorer" "Open file..." weather.arff "Classify" "Chooes" "tree""j48" Copyright (C) 2008 i-library MOT 7
8 "start" Copyright (C) 2008 i-library MOT 8
9 Weka Copyright (C) 2008 i-library MOT 9
10 August 20, 2007 Copyright (C) 2008 i-library MOT 10
11 WEKA Copyright (C) 2008 i-library MOT 11
12 August 21, 2007 Weka Weka Weka Kappa statistic κ Confusion Matrix TP Rate yes Positive Accuracy Correctly Classified Instances"9" % 14 9 yes Kappa statistic κ TP Rate Copyright (C) 2008 i-library MOT 12
13 2-2 August 23, 2007 Accuracy ml n 1020 play tml Copyright (C) 2008 i-library MOT 13
14 100%!! 60% A,B,C,D,E,F,G,H,I,J A I J BJ A B B Correctly Classified Instances % Weka Copyright (C) 2008 i-library MOT 14
15 2-3 August 26, 2007 Weka Copyright (C) 2008 i-library MOT 15
16 "more" binarysplits= minnumobj= instancesn seed=seed subtreeraising unpruned= "Correctly Classified Instances" CSV weather.arff weather.csv CSV Weka weka 6 "play" "?" "westher_2.csv" "Correctly Classified Instances" "More options..." "Output predictions" "OK" start Copyright (C) 2008 i-library MOT 16
17 "actual" "predicted" "?" "?" "yes" Weka Copyright (C) 2008 i-library MOT 17
18 2-4 August 27, 2007 weka play J48 "Bayesian" Wikipedia y=ax+b y=ax+b a,b Classify:BaysNet:GeneticSearch HillClimber J48 play windy humidity outlook temperature play Copyright (C) 2008 i-library MOT 18
19 Bayesian Bayesian ( Weka Copyright (C) 2008 i-library MOT 19
20 3. August 30, 2007 weka Amazon CSV p yes or no yes "?" Copyright (C) 2008 i-library MOT 20
21 Preprocess:Open files... Classify:lazy:IBk More optios.. Output predictions "OK" "Test option" "Use training set" "start" "yes" Weka Copyright (C) 2008 i-library MOT 21
22 September 6, 2007 k-nn k-nearest-neighbor k k k k k k=1 3 5 Copyright (C) 2008 i-library MOT 22
23 Amazon Weka Copyright (C) 2008 i-library MOT 23
24 4. September 9, 2007 Preprocess:Open file... Classify:MultilayerPerceptron weka weather.arff csv "?" Copyright (C) 2008 i-library MOT 24
25 Cross-validation Folds 10 More options...output predictions OK (Nom)play "Start" output ? 2:yes * Correctly Classified Instances % "yes" GUI input output Copyright (C) 2008 i-library MOT 25
26 input tanh Correctly Classified Instances Copyright (C) 2008 i-library MOT 26
27 Weka September 22, 2007 "accuracy" weka "Experimenter" weka "weather.arff" IBk Labels & Links Weka Copyright (C) 2008 i-library MOT 27
28 Copyright (C) 2008 i-library MOT 28
29 "accuracy" IBk Weka Copyright (C) 2008 i-library MOT 29
30 5. September 26, 2007 Z Y 2 weka Copyright (C) 2008 i-library MOT 30
31 Copyright (C) 2008 i-library MOT 31
32 support confidence Copyright (C) 2008 i-library MOT 32
33 "support""confidence" Copyright (C) 2008 i-library MOT 33
34 October 20, 2007 support confidence support confidence support confidence AB A B A B 100 A 50 B 20 support(a) A A P(A) 100 P(A)=50/100=0.5 P(B)=20/100=0.2 A B AB 10 P(AB) P(AB)=10/100=0.1 Copyright (C) 2008 i-library MOT 34
35 confidence AB p(b A) p(b A)=10/50=0.2 A B? Lift Lift (AB) = confidence(ab) / confidence(b)= 0.2/0.2=1.0 Lift (BA) = confidence(ba) / confidence(a)= 0.5/0.5= support confidence support20% confidende70% try Weka apriori program Windows ROC Lift chart Copyright (C) 2008 i-library MOT 35
36 1 ROC lift chart November 20, 2007 Weka Labels lift chart ROC weka weather.arff J48 Classify:choose:trees:J48 Start Visualize threshold curveyes play =yes "1" "2" Copyright (C) 2008 i-library MOT 36
37 ROC X :False Positive RateY :True Positive Rate lift chart X :Instance numbery :True Positive Rate Copyright (C) 2008 i-library MOT 37
38 Copyright (C) 2008 i-library MOT 38
39 2 December 11, 2007 AD 4 A A1A2A3 BD A : A:A1 +2 B:B4 +5 C:C1 +3 D:D2 +3 AD +13 A,B,C,D weka weka "?" "More options..." "Output predictions" "Test options "choose" ""Use training set" Copyright (C) 2008 i-library MOT 39
40 csv "?" Copyright (C) 2008 i-library MOT 40
41 ? 100 Weka Copyright (C) 2008 i-library MOT 41
42 + PC Copyright (C) 2008 i-library MOT 42
43 F%E3%83%9E%E3%82%A4%E3%83%8B%E3%83%B3%E3%82%B 0 Wiki " " "weka" WEKA kdnuggets WEKAMachine Learning Project "software" "Download" weka TeraPad SDI WEKA apriori κ ml Copyright (C) 2008 i-library MOT 43
44 Copyright (C) 2008 i-library MOT 44
PowerPoint Presentation
AI Programming data mining ( Plug in Weka to Eclipse) Review of Identification Tree Run bouncing ball in Weka Run bouncing ball in Eclipse How about color? weight? rubber? Please write down their formulae.
フリーソフトではじめる機械学習入門 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. このサンプルページの内容は, 初版 1 刷発行時のものです.
フリーソフトではじめる機械学習入門 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. http://www.morikita.co.jp/books/mid/085211 このサンプルページの内容は, 初版 1 刷発行時のものです. Weka Weka 2014 2 i 1 1 1.1... 1 1.2... 3 1.3... 6 1.3.1 7 1.3.2 11
Microsoft PowerPoint - 03Weka.ppt
情報意味論 (3) Weka の紹介 WEKA: Explorer WEKA: Experimenter Preslav Nakov (October 6, 2004) http://www.sims.berkeley.edu/courses/is290-2/f04/lectures/lecture11.ppt WEKA: 使ってみよう Eibe Frank http://prdownloads.sourceforge.net/weka/weka.ppt
GOVERNOR'S No.1 JULY 2 GOVERNOR'S No.1 JULY 3 GOVERNOR'S No.1 JULY 4 GOVERNOR'S No.1 JULY 5 GOVERNOR'S No.1 JULY 6 GOVERNOR'S No.1 JULY 7 GOVERNOR'S No.1 JULY 8 GOVERNOR'S No.1 JULY 9 GOVERNOR'S No.1 JULY
1 May 2011
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Microsoft PowerPoint - 02Weka.ppt
Weka Weka: A brief introdcution Akito Sakurai Keio University A software to be used this time Devloped by University of Waikato, New Zealand An abbreviation of Waikato Environment of Knowledge Analysis
プロジェクトガイダンス
Weka のインストールとデータ分析導入 2015 年 5 月 1 日 とその前に レポート等ファイル名について 提出レポートのファイル名は以下の規則に従ってつけてください 学籍番号氏名 _ プロジェクト # 番号.docx 例 )13T9001 佐藤温 _ プロジェクト #03.docx プロジェクトで扱うデータマイニングツール プロジェクトでは Weka というワイカト大学で開発された Java
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005 11 http://www.hyuki.com/girl/ http://www.hyuki.com/story/tetora.html http://www.hyuki.com/ Hiroshi Yuki c 005, All rights reserved. 1 1 3 (a + b)(a b) = a b (x + y)(x y) = x y a b x y a b x y 4 5 6
スライド 1
22 YES NO ( ) 15 4 3 15 5 : Wikipedia text mining : Wikipedia Data mining DM heuristic, 1: 2: 1: 2: 1: 2: 3: 4: 5: 6: 7: 8: 1: 2: 1: 2: 1: 2: 3: 4: 5: 1: 2: 1: 2: 3: 4: 5: 1: 2: 3: 1: 2: 1: 2: 3:
Introduction Purpose This training course demonstrates the use of the High-performance Embedded Workshop (HEW), a key tool for developing software for
Introduction Purpose This training course demonstrates the use of the High-performance Embedded Workshop (HEW), a key tool for developing software for embedded systems that use microcontrollers (MCUs)
Microsoft PowerPoint - Weka_ _間帅.pptx
2017/10/20 データ解析実践演習 Wekaによるデータ分析と応用 データ解析の手順 収集したデータはそれぞれ形式が違う データを整形し 特徴を見極める 例えば Wekaに付属のデータは データ名 内容 特徴 Breast-canser 乳がんの再発 ラベル 正解情報 クラス 2値 Contact-lenses コンタクトレンズの推薦 ラベル クラス 3値 Cpu CPUの性能評価 数値 数値
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スライド 1
大規模離散計算科学特論 11 月 24 日 白井康之 独 科学技術振興機構 ERATO 湊離散構造処理系プロジェクト shirai@erato.ist.hokudai.ac.jp 1 講義内容 自己紹介 手法編 1-1 はじめに 全体概要 1-2 頻出パタンマイニングとその周辺 1-3 決定木分析法とその周辺 事例紹介 予定 ザッピングデータ解析 実世界情報による行動パタンの分析 インターネット情報を用いた技術トレンド解析
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