The document discusses various aspects of data mining and machine learning, including methodologies like SEMMA, CRISP-DM, and KDD. It covers different learning paradigms such as supervised, unsupervised, and reinforcement learning, along with evaluation metrics like precision, recall, and ROC-AUC. Additionally, it references Python libraries and tools for implementing machine learning techniques.

1. @canard0328
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http://nbviewer.ipython.org/gist/canard0328/6f44229365f53b7bd30f/
http://nbviewer.ipython.org/gist/canard0328/a5911ee5b4bf1a07fbcb/
https://gist.github.com/canard0328/07a65584c134a2700725
https://gist.github.com/canard0328/b2f8aec2b9c286f53400

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Sample
Explore
Modify
Model
Assess
Sample Explore Modify Model Assess
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SEMMA

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CRISPLDM CRossLIndustryNStandardNProcessNforNDataNMining
BusinessNUnderstanding
DataNUnderstanding
DataNPreparation
Modeling
Evaluation
Deployment
KDD KnowledgeNDiscoveryNinNDatabases
Selection
Preprocessing
Transformation
DataNMining
Interpretation/Evaluation
KKD Keiken,NKan andNDokyo

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http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic3.csv
(DataNobtainedNfromNhttp://biostat.mc.vanderbilt.edu/DataSets)
> data = read.csv(“titanic3.csv”,
+ stringsAsFactors=F, na.strings=c("","NA"))
>>> import pandas as pd
>>> data = pd.read_csv(‘titanic3.csv')
Sample Explore Modify
Assess Model

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http://en.wikipedia.org/wiki/Feature_hashing

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AndrewNNg
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Sample Explore Modify
Assess Model

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supervisedNlearning
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• classification
• regression
unsupervisedNlearning
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•
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• outlierNdetection

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• semiLsupervisedNlearning
• reinforcementNlearning
• activeNlearning
• onlineNlearning
• transferNlearning

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•
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• KLmeansN
•
• Apriori
• OneLclassNSVM

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Sample Explore Modify
Assess Model

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(MeanNabsoluteNerror)
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(MeanNsquare(d)Nerror)
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RootNMeanNSquare(d)N Error
R2(CoefficientNofNdetermination)
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(Accuracy)
(ErrorNrate)
1N
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(ConfusionNmatrix)
(Positive) 26 5 8 6
(TrueNpositiveN:NTP) (FalseNnegativeN:NFN)
(FalseNpositiveN:NFP) 4: 6 96 5 8 6 / 42
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(Precision)
TP/(TPN+NFP)
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(Recall)
TP/(TPN+NFN)
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F (F1Nscore,NFLmeasure)
2 ( )N/N( ) P 2
3 TP FN
2 FP 42

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(True Positive Rate)
TP/(TPN+NFN)
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(False Positive Rate)
FP/(FPN+NTN)
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P 2
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2 FP 42

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ROC AUC

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n
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>Nclf =NSVC().fit(X,Ny)

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>Nclf =NSVC(kernel=‘rbf’,NC=1.0Ngamma=0.1).fit(X,Ny)

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(Cross validation)
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4. A C,E D
5. A D E
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https://www.linkedin.com/pulse/inconvenientLtruthLdataLscienceLkamilLbartocha

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MALSS
(MachineNLearningNSupportNSystem)
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Python
•
•
•
•
•

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MALSS
> pip install –U malss
> from malss import MALSS
> clf = MALSS('classification‘, lang=‘jp’)
> clf.fit(X, y, ‘report_output_dir')
> clf.make_sample_code('sample_code.py')

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MALSS

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MALSS

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F.NProvost
Coursera:=Machine=Learning
AndrewNNg https://www.coursera.org/course/ml
scikit0learn=Tutorials
http://scikitLlearn.org/stable/tutorial/
Tutorial:=Machine=Learning=for=Astronomy=with=Scikit0learn
http://www.astroml.org/sklearn_tutorial/

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MALSS=(Machine=Learning=Support=System)
https://pypi.python.org/pypi/malss/
https://github.com/canard0328/malss
Python MALSS
Qiita http://qiita.com/canard0328/items/fe1ccd5721d59d76cc77
Python MALSS
Qiita http://qiita.com/canard0328/items/5da95ff4f2e1611f87e1
Python MALSS
Qiita http://qiita.com/canard0328/items/3713d6758fe9c045a19d

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1.
SEMMA CRISPLDM KDD KKD
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