added scoring
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@ -6,7 +6,7 @@ import csv
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.preprocessing import MinMaxScaler
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from enum import Enum
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from enum import Enum
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import random
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import random
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from sklearn.metrics import confusion_matrix
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from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score
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class Tree(Enum):
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class Tree(Enum):
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ACCASIA = 0
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ACCASIA = 0
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@ -103,3 +103,10 @@ for validateId in range(0, tags_len - 1):
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sns.heatmap(confusion_matrix(tag_true, tag_predict), annot=True)
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sns.heatmap(confusion_matrix(tag_true, tag_predict), annot=True)
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plt.title( "Confusion Matrix KNN" )
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plt.title( "Confusion Matrix KNN" )
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plt.show()
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plt.show()
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# Score
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print("Accuracy score", accuracy_score(tag_true, tag_predict))
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print("Precision score (macro)", precision_score(tag_true, tag_predict, average='macro'))
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print("Precision score (micro)", precision_score(tag_true, tag_predict, average='micro'))
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print("Recall score (macro)", recall_score(tag_true, tag_predict, average='macro'))
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print("Recall score (micro)", recall_score(tag_true, tag_predict, average='micro'))
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