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BigDataAnalytics Homework2

####Team Member : B10015035 蘇育民

B10015036 陳尹清

B10015037 蘇斌綸

##Introduction

###Environment ####Spark 1.3.1 Method => Logistic Regression
Classes => 2(normal and abnormal)

####Execute

$SPARK_HOME/bin/spark-submit kddtrain.py

####Training Data (Kddcup99) Training Data only use two labels(0 or 1)
Testing Data use corrected data

Result

Precision = 0.998714041244
Recall = 0.80158148911
Training Error = 0.00431342199165
tp = 3909555, tn = 16095, fp = 5034, fn = 967747

Confusion Matrix

Rows/Cols 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
1 59627.0 4.0 0.0 0.0 1.0 631.0 3.0 17.0 37.0 66.0 7.0 0.0 0.0 6.0 0.0 99.0 0.0 2.0 0.0 0.0 0.0 0.0
2 18.0 4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
5 16.0 0.0 0.0 0.0 57952.0 0.0 0.0 0.0 0.0 23.0 0.0 0.0 0.0 0.0 0.0 10.0 0.0 0.0 0.0 0.0 0.0 0.0
6 94.0 0.0 0.0 0.0 0.0 163994.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
7 4357.0 0.0 0.0 0.0 0.0 0.0 4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.0 0.0 0.0 0.0 0.0 0.0 0.0
8 2.0 0.0 0.0 0.0 0.0 0.0 0.0 81.0 0.0 0.0 4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.0 8.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
10 17.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 336.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
11 5.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 297.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
12 4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
13 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
14 27.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1071.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
16 148.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 18.0 2.0 0.0 0.0 0.0 0.0 1464.0 0.0 1.0 0.0 0.0 0.0 0.0
17 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
18 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 83.0 0.0 0.0 0.0 0.0
19 18.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
20 1595.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
21 10.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0
22 17561.0 1.0 0.0 0.0 342.0 0.0 3.0 0.0 0.0 25.0 23.0 0.0 0.0 0.0 0.0 626.0 1.0 118.0 0.0 0.0 0.0 0.0
precision 0.7140188482678513 0.4444444444444444 0.0 0.0 0.9941161334591303 0.9961488932623855 0.4 0.7941176470588235 0.17777777777777778 0.7164179104477612 0.8658892128279884 1.0 0.0 0.9925857275254866 0.0 0.6645483431684067 0.0 0.40487804878048783 0.0 0.0 0.0 0.0
recall 0.9855702479338843 0.18181818181818182 0.0 0.0 0.9991551869795349 0.9994088645934268 9.163802978235968E-4 0.9310344827586207 0.6666666666666666 0.9491525423728814 0.9705882352941176 0.5555555555555556 0.0 0.9754098360655737 0.0 0.8965094917330068 0.0 0.9880952380952381 0.0 0.0 0.0 0.0

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