Yeah that is the rounding of using %2f in the classification report.
On 06/17/2015 09:20 AM, Joel Nothman wrote:
To me, those numbers appear identical at 2 decimal places.
On 17 June 2015 at 23:04, Herbert Schulz <hrbrt....@gmail.com
<mailto:hrbrt....@gmail.com>> wrote:
Hello everyone,
i wrote a function to calculate the sensitivity,specificity,
ballance accuracy and accuracy from a confusion matrix.
Now i have a Problem, I'm getting different values when I'm
comparing my Values with those from the
metrics.classification_report function.
The general problem ist, my predicted sensitivity is in the
classification report the precision value. I'm computing every
sensitivity with the one vs all approach. So e.g. Class 1 ==
true, class 2,3,4,5 are the rest (not true).
I did this only to get the specificity, and to compare if i
computed everything right.
----------- ensemble -----------
precision recall f1-score support
1.0 *0.56 * 0.68 0.61 129
2.0 *0.28* 0.15 0.20 78
3.0 *0.45 * 0.47 0.46 116
4.0 *0.29* 0.05 0.09 40
5.0 *0.44 * 0.66 0.53 70
avg / total 0.43 0.47 0.43 433
Class: 1
sensitivity:*0.556962025316*
specificity: 0.850909090909
ballanced accuracy: 0.703935558113
Class: 2
sensitivity:*0.279069767442*
specificity: 0.830769230769
ballanced accuracy: 0.554919499106
Class: 3
sensitivity*:0.446280991736*
specificity: 0.801282051282
ballanced accuracy: 0.623781521509
Class: 4
sensitivity:*0.285714285714*
specificity: 0.910798122066
ballanced accuracy: 0.59825620389
Class: 5
sensitivity:*0.442307692308*
specificity: 0.927051671733
ballanced accuracy: 0.68467968202
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