Changeset 10126:e4ccdbfc6e08 in orange


Ignore:
Timestamp:
02/08/12 20:24:54 (2 years ago)
Author:
Matija Polajnar <matija.polajnar@…>
Branch:
default
rebase_source:
282fb2b853900ac91754e8ca441b954995908db8
Message:

Shorten the mlc-evaluate.py documentation script, which also reduces regression testing time for a lot.

Files:
2 edited

Legend:

Unmodified
Added
Removed
  • Orange/testing/regression/results_reference/mlc-evaluate.py.txt

    r9954 r10126  
    44recall= [0.594435075885329] 
    55 
    6 loss= [0.2034851039910062] 
    7 accuracy= [0.4852445193929173] 
    8 precision= [0.6455874086565487] 
    9 recall= [0.5486228218100055] 
    10  
    11 loss= [0.21768018018018018] 
    12 accuracy= [0.4780349099099102] 
    13 precision= [0.6189752252252263] 
    14 recall= [0.5553490990990999] 
    15  
    16 loss= [0.2703766160764474] 
    17 accuracy= [0.32192242833052276] 
    18 precision= [0.47034851039910053] 
    19 recall= [0.3797077009555932] 
    20  
    21 loss= [0.24423833614390106] 
    22 accuracy= [0.40668915120854426] 
    23 precision= [0.5784148397976391] 
    24 recall= [0.4710511523327713] 
    25  
    26 loss= [0.22596964586846544] 
    27 accuracy= [0.4644463181562672] 
    28 precision= [0.6273187183811131] 
    29 recall= [0.5348510399100616] 
    30  
    31 loss= [0.2181000562113547] 
    32 accuracy= [0.47976391231028676] 
    33 precision= [0.6514896008993819] 
    34 recall= [0.5483417650365374] 
    35  
    36 loss= [0.22484541877459246] 
    37 accuracy= [0.47372119168071913] 
    38 precision= [0.6242270938729625] 
    39 recall= [0.5489038785834737] 
    40  
    41 loss= [0.21247892074198987] 
    42 accuracy= [0.5081506464305793] 
    43 precision= [0.6624508150646434] 
    44 recall= [0.5868465430016865] 
    45  
    46 loss= [0.2034851039910062] 
    47 accuracy= [0.5223440134907252] 
    48 precision= [0.6750983698707141] 
    49 recall= [0.6011804384485666] 
    50  
    51 loss= [0.20320404721753793] 
    52 accuracy= [0.5168634064080946] 
    53 precision= [0.6762225969645872] 
    54 recall= [0.5888139404159641] 
    55  
    56 loss= [0.20460933108487914] 
    57 accuracy= [0.5036537380550874] 
    58 precision= [0.6613265879707703] 
    59 recall= [0.5674536256323779] 
    60  
    61 loss= [0.20489038785834737] 
    62 accuracy= [0.5168634064080946] 
    63 precision= [0.6790331646992694] 
    64 recall= [0.594435075885329] 
    65  
  • docs/reference/rst/code/mlc-evaluate.py

    r9823 r10126  
    1717res = Orange.evaluation.testing.cross_validation(learners, emotions) 
    1818print_results(res) 
    19  
    20 res = Orange.evaluation.testing.leave_one_out(learners, emotions) 
    21 print_results(res) 
    22  
    23 res = Orange.evaluation.testing.proportion_test(learners, emotions, 0.5) 
    24 print_results(res) 
    25  
    26 reses = Orange.evaluation.testing.learning_curve(learners, emotions) 
    27 for res in reses: 
    28     print_results(res) 
Note: See TracChangeset for help on using the changeset viewer.