Changeset 9834:e01d63ac99a9 in orange


Ignore:
Timestamp:
02/06/12 20:11:17 (2 years ago)
Author:
Miha Stajdohar <miha.stajdohar@…>
Branch:
default
Message:

New changes.

Location:
Orange/testing/regression/results_modules
Files:
2 edited

Legend:

Unmodified
Added
Removed
  • Orange/testing/regression/results_modules/tree8.py.txt

    r9689 r9834  
    11m = 0.000: 239 nodes, 134 leaves 
    2 m = 0.000: 228 nodes, 128 leaves 
     2m = 0.000: 183 nodes, 104 leaves 
    33m = 0.100: 173 nodes, 99 leaves 
    44m = 0.500: 179 nodes, 102 leaves 
  • Orange/testing/regression/results_modules/tuning1.py.txt

    r9689 r9834  
    1 *** optimization  1: [0.97058829757682885]: 
    2 *** optimization  2: [0.97642948164590782]: 
    3 *** optimization  3: [0.98338801755375926]: 
    4 *** optimization  4: [0.98788177744892502]: 
    5 *** optimization  5: [0.98894238973780235]: 
    6 *** optimization  10: [0.98692031923626455]: 
    7 *** optimization  15: [0.98842239138206578]: 
    8 *** optimization  20: [0.97804067310171638]: 
     1*** optimization  1: [0.9706992853681718]: 
     2*** optimization  2: [0.9743207136103917]: 
     3*** optimization  3: [0.9833880175537593]: 
     4*** optimization  4: [0.987881777448925]: 
     5*** optimization  5: [0.9889423897378024]: 
     6*** optimization  10: [0.9869203192362646]: 
     7*** optimization  15: [0.9884223913820658]: 
     8*** optimization  20: [0.9780406731017164]: 
    99*** Optimal parameter: minSubset = 5 
    1010Optimal setting:  5 
    11 *** optimization  1: [0.98321908602150532]: 
    12 *** optimization  2: [0.97819892473118264]: 
     11*** optimization  1: [0.9832190860215053]: 
     12*** optimization  2: [0.9781989247311826]: 
    1313*** optimization  3: [0.9912679211469535]: 
    1414*** optimization  4: [0.9937656810035842]: 
    15 *** optimization  5: [0.99075044802867385]: 
     15*** optimization  5: [0.9907504480286738]: 
    1616*** optimization  10: [0.9872647849462366]: 
    17 *** optimization  15: [0.98976926523297493]: 
    18 *** optimization  20: [0.99105062724014337]: 
     17*** optimization  15: [0.9897692652329749]: 
     18*** optimization  20: [0.9910506272401434]: 
    1919*** Optimal parameter: minSubset = 4 
    20 *** optimization  1: [0.97296370967741941]: 
    21 *** optimization  2: [0.97278673835125462]: 
    22 *** optimization  3: [0.98086245519713267]: 
    23 *** optimization  4: [0.98209901433691749]: 
    24 *** optimization  5: [0.98543682795698928]: 
    25 *** optimization  10: [0.98856854838709673]: 
    26 *** optimization  15: [0.99162634408602157]: 
    27 *** optimization  20: [0.98600806451612899]: 
     20*** optimization  1: [0.9729637096774194]: 
     21*** optimization  2: [0.9727867383512546]: 
     22*** optimization  3: [0.9808624551971327]: 
     23*** optimization  4: [0.9820990143369175]: 
     24*** optimization  5: [0.9854368279569893]: 
     25*** optimization  10: [0.9885685483870967]: 
     26*** optimization  15: [0.9916263440860216]: 
     27*** optimization  20: [0.986008064516129]: 
    2828*** Optimal parameter: minSubset = 15 
    29 *** optimization  1: [0.98023073476702494]: 
    30 *** optimization  2: [0.98306899641577061]: 
    31 *** optimization  3: [0.98245295698924728]: 
     29*** optimization  1: [0.9802307347670249]: 
     30*** optimization  2: [0.9830689964157706]: 
     31*** optimization  3: [0.9824529569892473]: 
    3232*** optimization  4: [0.9896012544802868]: 
    33 *** optimization  5: [0.98472670250896055]: 
    34 *** optimization  10: [0.98965277777777783]: 
    35 *** optimization  15: [0.98743503584229386]: 
    36 *** optimization  20: [0.97437948028673826]: 
     33*** optimization  5: [0.9847267025089605]: 
     34*** optimization  10: [0.9896527777777778]: 
     35*** optimization  15: [0.9874350358422939]: 
     36*** optimization  20: [0.9743794802867383]: 
    3737*** Optimal parameter: minSubset = 10 
    38 *** optimization  1: [0.96825044802867388]: 
    39 *** optimization  2: [0.97539202508960576]: 
    40 *** optimization  3: [0.97483422939068098]: 
    41 *** optimization  4: [0.98026657706093201]: 
    42 *** optimization  5: [0.97956765232974918]: 
     38*** optimization  1: [0.9682504480286739]: 
     39*** optimization  2: [0.9763328853046596]: 
     40*** optimization  3: [0.974834229390681]: 
     41*** optimization  4: [0.980266577060932]: 
     42*** optimization  5: [0.9795676523297492]: 
    4343*** optimization  10: [0.9769332437275986]: 
    44 *** optimization  15: [0.97734543010752684]: 
     44*** optimization  15: [0.9773454301075268]: 
    4545*** optimization  20: [0.9740815412186381]: 
    4646*** Optimal parameter: minSubset = 4 
    47 *** optimization  1: [0.96364247311827955]: 
    48 *** optimization  2: [0.974209229390681]: 
    49 *** optimization  3: [0.97841621863799277]: 
    50 *** optimization  4: [0.98721102150537632]: 
    51 *** optimization  5: [0.98688396057347672]: 
    52 *** optimization  10: [0.98780689964157697]: 
    53 *** optimization  15: [0.98020833333333335]: 
    54 *** optimization  20: [0.97671370967741944]: 
     47*** optimization  1: [0.9640591397849462]: 
     48*** optimization  2: [0.9741397849462365]: 
     49*** optimization  3: [0.9783467741935483]: 
     50*** optimization  4: [0.9872110215053763]: 
     51*** optimization  5: [0.9868839605734767]: 
     52*** optimization  10: [0.987806899641577]: 
     53*** optimization  15: [0.9802083333333333]: 
     54*** optimization  20: [0.9767137096774194]: 
    5555*** Optimal parameter: minSubset = 10 
    56 *** optimization  1: [0.97435707885304657]: 
    57 *** optimization  2: [0.97433705471435883]: 
    58 *** optimization  3: [0.97782107197717805]: 
    59 *** optimization  4: [0.97559295223465736]: 
    60 *** optimization  5: [0.98345644978421476]: 
    61 *** optimization  10: [0.98451827774120404]: 
    62 *** optimization  15: [0.98160682283666145]: 
     56*** optimization  1: [0.9743570788530466]: 
     57*** optimization  2: [0.9743370547143588]: 
     58*** optimization  3: [0.977821071977178]: 
     59*** optimization  4: [0.9755929522346574]: 
     60*** optimization  5: [0.9834564497842148]: 
     61*** optimization  10: [0.984518277741204]: 
     62*** optimization  15: [0.9816068228366615]: 
    6363*** optimization  20: [0.9802781892326824]: 
    6464*** Optimal parameter: minSubset = 10 
    6565*** optimization  1: [0.9764701740911419]: 
    66 *** optimization  2: [0.98519868700168234]: 
    67 *** optimization  3: [0.98775030173359668]: 
    68 *** optimization  4: [0.98942574610489364]: 
    69 *** optimization  5: [0.98909228476336764]: 
    70 *** optimization  10: [0.98262768817204293]: 
    71 *** optimization  15: [0.98151337685611872]: 
    72 *** optimization  20: [0.98251312083973374]: 
     66*** optimization  2: [0.9851986870016823]: 
     67*** optimization  3: [0.9877503017335967]: 
     68*** optimization  4: [0.9894257461048936]: 
     69*** optimization  5: [0.9890922847633676]: 
     70*** optimization  10: [0.9826276881720429]: 
     71*** optimization  15: [0.9815133768561187]: 
     72*** optimization  20: [0.9825131208397337]: 
    7373*** Optimal parameter: minSubset = 4 
    74 *** optimization  1: [0.98090168056469895]: 
    75 *** optimization  2: [0.99166556945358786]: 
    76 *** optimization  3: [0.98834444261575594]: 
    77 *** optimization  4: [0.98837159863945589]: 
    78 *** optimization  5: [0.99087895911052581]: 
    79 *** optimization  10: [0.98963769109794453]: 
    80 *** optimization  15: [0.98902969790066564]: 
     74*** optimization  1: [0.980901680564699]: 
     75*** optimization  2: [0.9916655694535879]: 
     76*** optimization  3: [0.9883444426157559]: 
     77*** optimization  4: [0.9884388029405311]: 
     78*** optimization  5: [0.9908789591105258]: 
     79*** optimization  10: [0.9896376910979445]: 
     80*** optimization  15: [0.9890296979006656]: 
    8181*** optimization  20: [0.9841350760734402]: 
    8282*** Optimal parameter: minSubset = 2 
    83 *** optimization  1: [0.97177195340501799]: 
    84 *** optimization  2: [0.97940412186379944]: 
    85 *** optimization  3: [0.98024641577060934]: 
    86 *** optimization  4: [0.98027105734767028]: 
    87 *** optimization  5: [0.98310707885304649]: 
    88 *** optimization  10: [0.98413978494623655]: 
    89 *** optimization  15: [0.98651209677419371]: 
    90 *** optimization  20: [0.97995519713261658]: 
     83*** optimization  1: [0.971771953405018]: 
     84*** optimization  2: [0.9794041218637994]: 
     85*** optimization  3: [0.9802464157706093]: 
     86*** optimization  4: [0.9802710573476703]: 
     87*** optimization  5: [0.9831070788530465]: 
     88*** optimization  10: [0.9841397849462366]: 
     89*** optimization  15: [0.9865120967741937]: 
     90*** optimization  20: [0.9799551971326166]: 
    9191*** Optimal parameter: minSubset = 15 
    92 *** optimization  1: [0.97084229390681009]: 
    93 *** optimization  2: [0.97555107526881724]: 
    94 *** optimization  3: [0.98384184587813628]: 
    95 *** optimization  4: [0.97722222222222244]: 
    96 *** optimization  5: [0.98428539426523298]: 
     92*** optimization  1: [0.9708422939068101]: 
     93*** optimization  2: [0.9755510752688172]: 
     94*** optimization  3: [0.9838418458781363]: 
     95*** optimization  4: [0.9772222222222224]: 
     96*** optimization  5: [0.984285394265233]: 
    9797*** optimization  10: [0.989247311827957]: 
    9898*** optimization  15: [0.987780017921147]: 
    99 *** optimization  20: [0.98074148745519696]: 
     99*** optimization  20: [0.980741487455197]: 
    100100*** Optimal parameter: minSubset = 10 
    101 Untuned tree: 0.925 
     101Untuned tree: 0.926 
    102102Tuned tree: 0.983 
    103 *** optimization  1: [0.97058829757682885]: 
    104 *** optimization  2: [0.97642948164590782]: 
    105 *** optimization  3: [0.98338801755375926]: 
    106 *** optimization  4: [0.98788177744892502]: 
    107 *** optimization  5: [0.98894238973780235]: 
    108 *** optimization  10: [0.98692031923626455]: 
    109 *** optimization  15: [0.98842239138206578]: 
    110 *** optimization  20: [0.97804067310171638]: 
     103*** optimization  1: [0.9706992853681718]: 
     104*** optimization  2: [0.9743207136103917]: 
     105*** optimization  3: [0.9833880175537593]: 
     106*** optimization  4: [0.987881777448925]: 
     107*** optimization  5: [0.9889423897378024]: 
     108*** optimization  10: [0.9869203192362646]: 
     109*** optimization  15: [0.9884223913820658]: 
     110*** optimization  20: [0.9780406731017164]: 
    111111*** Optimal parameter: ['split.continuousSplitConstructor.minSubset', 'split.discreteSplitConstructor.minSubset'] = 5 
    112112Optimal setting:  5.0 
Note: See TracChangeset for help on using the changeset viewer.