MMA classifier
If it looks like a duck, swims like a duck, and quacks like a duck, then it probably is a duck.
Duck test
From Wikipedia, the free encyclopedia
�Mixed Martial Arts = Judo + boxing + karate + kickboxing + greco-roman wrestling + jiu-jitsu + sambo…�����
Het idee:
Wat weet ik?
Doel
Rijk worden ☺
maar ook leren…
Python
API, JSON
Toepassing ML
Python libraries��Web scraper �BeautifulSoup ��ML classifier�Scikit-learn��Automatic betting �Urllib (Betfair Eschange API� via Jason-RPC)
Webscraping
http://www.sherdog.com/robots.txt
ML: Data, data, data
Dummy (uniform)��Dummy (most frequent)��DecisionTree��Naive Bayes��K-Nearest Neighbor��Neural Network��Support Vector Machine��Random Forest�
Accuracy per classifier, 10 splits Cross validation
K Nearest Neighbors��“Simple and lazy learner”�generaliseert (leert) niet, geen echte trainingsfase�heeft de hele training set nodig tijdens testing
K - te klein: overfitting�K - te groot ?��weights = 'distance'
Cross validation voor KNN
K: 1..19�
Confusion matrix
Accuracy: 0.60
Predicted
Actual 0 1
0 TN: 59 FP: 33
1 FN: 41 TP: 54
`precision recall f1-score support
0 0.59 0.64 0.61 92
1 0.62 0.57 0.59 95
avg /
total 0.61 0.60 0.60 187
Accuracy
It is the number of correct predictions made divided by the total number of predictions made
Precision
the number of positive predictions divided by the total number of positive class values predicted. It is also called the Positive Predictive Value (PPV).
Precision can be thought of as a measure of a classifiers exactness. A low precision can also indicate a large number of False Positives.
Recall
Recall is the number of True Positives divided by the number of True Positives and the number of False Negatives. Put another way it is the number of positive predictions divided by the number of positive class values in the test data. It is also called Sensitivity or the True Positive Rate.
Recall can be thought of as a measure of a classifiers completeness. A low recall indicates many False Negatives.
F1 Score
The F1 Score is the 2*((precision*recall)/(precision+recall)). It is also called the F Score or the F Measure. Put another way, the F1 score conveys the balance between the precision and the recall.
Real data…
MMA Classifier: Amirkhani wins !
Jason
{
"params": {
"filter": {
"eventTypeIds": [1]
}
},
"jsonrpc": "2.0",
"method": "SportsAPING/v1.0/listCompetitions",
"id": 1
}
Python rules! ��Long live Internet ☺�
Conclusie
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