Add oc1 and cart models

Update analysis and report mysql
This commit is contained in:
2021-03-03 12:47:47 +01:00
parent df42c0df74
commit 116db3f528
113 changed files with 1301 additions and 35 deletions

1
.gitignore vendored
View File

@@ -133,3 +133,4 @@ dmypy.json
.pre-commit-config.yaml .pre-commit-config.yaml
experimentation/.myconfig experimentation/.myconfig
experimentation/.tunnel experimentation/.tunnel
results

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@@ -1,18 +1,40 @@
import argparse
from typing import Tuple
from experimentation.Sets import Datasets from experimentation.Sets import Datasets
from experimentation.Utils import TextColor from experimentation.Utils import TextColor
from experimentation.Database import MySQL from experimentation.Database import MySQL
models = ["stree", "odte", "adaBoost", "bagging"] models = ["stree", "oc1", "cart", "odte", "adaBoost", "bagging"]
title = "Best model results" title = "Best model results"
lengths = (30, 9, 11, 11, 11, 11) lengths = (30, 9, 11, 11, 11, 11, 11, 11)
def report_header_content(title): def parse_arguments() -> Tuple[str, str, str, bool, bool]:
ap = argparse.ArgumentParser()
ap.add_argument(
"-e",
"--experiment",
type=str,
choices=["any", "gridsearch", "crossval"],
required=False,
default="any",
)
args = ap.parse_args()
return args.experiment
def report_header_content(title, experiment):
length = sum(lengths) + len(lengths) - 1 length = sum(lengths) + len(lengths) - 1
output = "\n" + "*" * length + "\n" output = "\n" + "*" * length + "\n"
num = (length - len(title) - 2) // 2 num = (length - len(title) - len(experiment) - 2) // 2
num2 = length - len(title) - 2 - 2 * num num2 = length - len(title) - len(experiment) - 5 - 2 * num
output += "*" + " " * num + title + " " * (num + num2) + "*\n" output += (
"*"
+ " " * num
+ f"{title} - {experiment}"
+ " " * (num + num2)
+ "*\n"
)
output += "*" * length + "\n\n" output += "*" * length + "\n\n"
lines = "" lines = ""
for item, data in enumerate(fields): for item, data in enumerate(fields):
@@ -22,8 +44,12 @@ def report_header_content(title):
return output return output
def report_header(exclude_params): def report_header(title, experiment):
print(TextColor.HEADER + report_header_content(title) + TextColor.ENDC) print(
TextColor.HEADER
+ report_header_content(title, experiment)
+ TextColor.ENDC
)
def report_line(line): def report_line(line):
@@ -59,13 +85,14 @@ def report_footer(agg):
) )
experiment = parse_arguments()
dbh = MySQL() dbh = MySQL()
database = dbh.get_connection() database = dbh.get_connection()
dt = Datasets(False, False, "tanveer") dt = Datasets(False, False, "tanveer")
fields = ("Dataset", "Reference") fields = ("Dataset", "Reference")
for model in models: for model in models:
fields += (f"{model}",) fields += (f"{model}",)
report_header(title) report_header(title, experiment)
color = TextColor.LINE1 color = TextColor.LINE1
agg = {} agg = {}
for item in [ for item in [
@@ -78,15 +105,16 @@ for item in [
agg[item]["worse"] = 0 agg[item]["worse"] = 0
for dataset in dt: for dataset in dt:
find_one = False find_one = False
# Look for max accuracy for any given dataset
line = {"dataset": color + dataset[0]} line = {"dataset": color + dataset[0]}
record = dbh.find_best(dataset[0], "any") record = dbh.find_best(dataset[0], "any", experiment)
max_accuracy = 0.0 if record is None else record[5] max_accuracy = 0.0 if record is None else record[5]
for model in models: for model in models:
record = dbh.find_best(dataset[0], model) record = dbh.find_best(dataset[0], model, experiment)
if record is None: if record is None:
line[model] = color + "-" * 9 + " " line[model] = color + "-" * 9 + " "
else: else:
reference = record[10] reference = record[13]
accuracy = record[5] accuracy = record[5]
find_one = True find_one = True
agg[model]["items"] += 1 agg[model]["items"] += 1

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@@ -0,0 +1,12 @@
Remapping class numbers:
0 To 1
1 To 3
Pruned decision tree written to csv/balance-scale.csv.dt.
Unpruned decision tree written to csv/balance-scale.csv.dt.unpruned.
fold 1: acc. = 91.20 #leaves = 16 max. depth = 9
fold 2: acc. = 86.40 #leaves = 30 max. depth = 9
fold 3: acc. = 96.00 #leaves = 20 max. depth = 8
fold 4: acc. = 92.80 #leaves = 17 max. depth = 5
fold 5: acc. = 94.40 #leaves = 22 max. depth = 7
accuracy = 92.16 #leaves = 21.00 max depth = 7.60
.79400000000000000000

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@@ -0,0 +1,11 @@
Remapping class numbers:
0 To 2
Pruned decision tree written to csv/balloons.csv.dt.
Unpruned decision tree written to csv/balloons.csv.dt.unpruned.
fold 1: acc. = 66.67 #leaves = 2 max. depth = 1
fold 2: acc. = 66.67 #leaves = 2 max. depth = 1
fold 3: acc. = 100.00 #leaves = 4 max. depth = 3
fold 4: acc. = 100.00 #leaves = 4 max. depth = 3
fold 5: acc. = 50.00 #leaves = 2 max. depth = 1
accuracy = 75.00 #leaves = 2.80 max depth = 1.80
.00400000000000000000

View File

@@ -0,0 +1,11 @@
Remapping class numbers:
0 To 2
Pruned decision tree written to csv/breast-cancer-wisc-diag.csv.dt.
Unpruned decision tree written to csv/breast-cancer-wisc-diag.csv.dt.unpruned.
fold 1: acc. = 92.92 #leaves = 12 max. depth = 6
fold 2: acc. = 94.69 #leaves = 11 max. depth = 5
fold 3: acc. = 92.92 #leaves = 12 max. depth = 5
fold 4: acc. = 92.04 #leaves = 15 max. depth = 7
fold 5: acc. = 91.45 #leaves = 7 max. depth = 3
accuracy = 92.79 #leaves = 11.40 max depth = 5.20
1.91800000000000000000

View File

@@ -0,0 +1,12 @@
Remapping class numbers:
0 To 1
1 To 2
Pruned decision tree written to csv/breast-cancer-wisc-prog.csv.dt.
Unpruned decision tree written to csv/breast-cancer-wisc-prog.csv.dt.unpruned.
fold 1: acc. = 82.05 #leaves = 2 max. depth = 1
fold 2: acc. = 71.79 #leaves = 13 max. depth = 10
fold 3: acc. = 82.05 #leaves = 17 max. depth = 7
fold 4: acc. = 51.28 #leaves = 16 max. depth = 7
fold 5: acc. = 78.57 #leaves = 14 max. depth = 8
accuracy = 73.23 #leaves = 12.40 max depth = 6.60
.92700000000000000000

View File

@@ -0,0 +1,12 @@
Remapping class numbers:
0 To 1
1 To 2
Pruned decision tree written to csv/breast-cancer-wisc.csv.dt.
Unpruned decision tree written to csv/breast-cancer-wisc.csv.dt.unpruned.
fold 1: acc. = 95.68 #leaves = 4 max. depth = 3
fold 2: acc. = 93.53 #leaves = 11 max. depth = 7
fold 3: acc. = 94.24 #leaves = 12 max. depth = 9
fold 4: acc. = 95.68 #leaves = 15 max. depth = 7
fold 5: acc. = 94.41 #leaves = 13 max. depth = 8
accuracy = 94.71 #leaves = 11.00 max depth = 6.80
1.02400000000000000000

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@@ -0,0 +1,12 @@
Remapping class numbers:
0 To 1
1 To 2
Pruned decision tree written to csv/breast-cancer.csv.dt.
Unpruned decision tree written to csv/breast-cancer.csv.dt.unpruned.
fold 1: acc. = 64.91 #leaves = 4 max. depth = 3
fold 2: acc. = 56.14 #leaves = 40 max. depth = 12
fold 3: acc. = 64.91 #leaves = 24 max. depth = 8
fold 4: acc. = 64.91 #leaves = 37 max. depth = 10
fold 5: acc. = 70.69 #leaves = 32 max. depth = 10
accuracy = 64.34 #leaves = 27.40 max depth = 8.60
.39700000000000000000

View File

@@ -0,0 +1,19 @@
Remapping class numbers:
8 To 1
5 To 2
1 To 3
7 To 4
9 To 5
0 To 7
2 To 8
4 To 9
3 To 10
Pruned decision tree written to csv/cardiotocography-10clases.csv.dt.
Unpruned decision tree written to csv/cardiotocography-10clases.csv.dt.unpruned.
fold 1: acc. = 78.12 #leaves = 19 max. depth = 7
fold 2: acc. = 78.35 #leaves = 144 max. depth = 15
fold 3: acc. = 79.29 #leaves = 134 max. depth = 12
fold 4: acc. = 77.65 #leaves = 138 max. depth = 14
fold 5: acc. = 80.28 #leaves = 146 max. depth = 14
accuracy = 78.74 #leaves = 116.20 max depth = 12.40
20.41800000000000000000

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@@ -0,0 +1,12 @@
Remapping class numbers:
0 To 2
2 To 3
Pruned decision tree written to csv/cardiotocography-3clases.csv.dt.
Unpruned decision tree written to csv/cardiotocography-3clases.csv.dt.unpruned.
fold 1: acc. = 88.47 #leaves = 14 max. depth = 7
fold 2: acc. = 91.06 #leaves = 63 max. depth = 12
fold 3: acc. = 91.76 #leaves = 59 max. depth = 10
fold 4: acc. = 89.41 #leaves = 58 max. depth = 14
fold 5: acc. = 90.85 #leaves = 55 max. depth = 12
accuracy = 90.31 #leaves = 49.80 max depth = 11.00
11.24900000000000000000

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@@ -0,0 +1,11 @@
Remapping class numbers:
0 To 2
Pruned decision tree written to csv/conn-bench-sonar-mines-rocks.csv.dt.
Unpruned decision tree written to csv/conn-bench-sonar-mines-rocks.csv.dt.unpruned.
fold 1: acc. = 68.29 #leaves = 2 max. depth = 1
fold 2: acc. = 70.73 #leaves = 21 max. depth = 9
fold 3: acc. = 78.05 #leaves = 16 max. depth = 8
fold 4: acc. = 65.85 #leaves = 12 max. depth = 6
fold 5: acc. = 65.91 #leaves = 15 max. depth = 6
accuracy = 69.71 #leaves = 13.20 max depth = 6.00
.52500000000000000000

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@@ -0,0 +1,12 @@
Remapping class numbers:
0 To 1
1 To 2
Pruned decision tree written to csv/cylinder-bands.csv.dt.
Unpruned decision tree written to csv/cylinder-bands.csv.dt.unpruned.
fold 1: acc. = 72.55 #leaves = 12 max. depth = 5
fold 2: acc. = 54.90 #leaves = 41 max. depth = 9
fold 3: acc. = 65.69 #leaves = 35 max. depth = 10
fold 4: acc. = 68.63 #leaves = 43 max. depth = 12
fold 5: acc. = 65.38 #leaves = 35 max. depth = 10
accuracy = 65.43 #leaves = 33.20 max depth = 9.20
2.12700000000000000000

13
data/oc1output/dermatology.txt Executable file
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@@ -0,0 +1,13 @@
Remapping class numbers:
0 To 2
2 To 3
3 To 5
5 To 6
Unpruned decision tree written to csv/dermatology.csv.dt.
fold 1: acc. = 98.63 #leaves = 6 max. depth = 3
fold 2: acc. = 97.26 #leaves = 6 max. depth = 3
fold 3: acc. = 93.15 #leaves = 6 max. depth = 3
fold 4: acc. = 93.15 #leaves = 9 max. depth = 6
fold 5: acc. = 97.30 #leaves = 8 max. depth = 5
accuracy = 95.90 #leaves = 7.00 max depth = 4.00
.54800000000000000000

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@@ -0,0 +1,12 @@
Remapping class numbers:
0 To 1
1 To 2
Pruned decision tree written to csv/echocardiogram.csv.dt.
Unpruned decision tree written to csv/echocardiogram.csv.dt.unpruned.
fold 1: acc. = 84.62 #leaves = 2 max. depth = 1
fold 2: acc. = 65.38 #leaves = 10 max. depth = 5
fold 3: acc. = 73.08 #leaves = 8 max. depth = 4
fold 4: acc. = 61.54 #leaves = 12 max. depth = 7
fold 5: acc. = 66.67 #leaves = 10 max. depth = 5
accuracy = 70.23 #leaves = 8.40 max depth = 4.40
.11500000000000000000

12
data/oc1output/fertility.txt Executable file
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@@ -0,0 +1,12 @@
Remapping class numbers:
0 To 1
1 To 2
Pruned decision tree written to csv/fertility.csv.dt.
Unpruned decision tree written to csv/fertility.csv.dt.unpruned.
fold 1: acc. = 95.00 #leaves = 2 max. depth = 1
fold 2: acc. = 70.00 #leaves = 8 max. depth = 4
fold 3: acc. = 75.00 #leaves = 5 max. depth = 3
fold 4: acc. = 80.00 #leaves = 9 max. depth = 5
fold 5: acc. = 75.00 #leaves = 7 max. depth = 4
accuracy = 79.00 #leaves = 6.20 max depth = 3.40
.05600000000000000000

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@@ -0,0 +1,12 @@
Remapping class numbers:
0 To 1
1 To 2
Pruned decision tree written to csv/haberman-survival.csv.dt.
Unpruned decision tree written to csv/haberman-survival.csv.dt.unpruned.
fold 1: acc. = 73.77 #leaves = 2 max. depth = 1
fold 2: acc. = 60.66 #leaves = 41 max. depth = 13
fold 3: acc. = 70.49 #leaves = 39 max. depth = 11
fold 4: acc. = 73.77 #leaves = 39 max. depth = 10
fold 5: acc. = 61.29 #leaves = 41 max. depth = 9
accuracy = 67.97 #leaves = 32.40 max depth = 8.80
.31700000000000000000

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@@ -0,0 +1,12 @@
Remapping class numbers:
0 To 1
1 To 2
Pruned decision tree written to csv/heart-hungarian.csv.dt.
Unpruned decision tree written to csv/heart-hungarian.csv.dt.unpruned.
fold 1: acc. = 86.21 #leaves = 4 max. depth = 3
fold 2: acc. = 67.24 #leaves = 19 max. depth = 6
fold 3: acc. = 68.97 #leaves = 30 max. depth = 9
fold 4: acc. = 74.14 #leaves = 26 max. depth = 9
fold 5: acc. = 74.19 #leaves = 19 max. depth = 7
accuracy = 74.15 #leaves = 19.60 max depth = 6.80
.48400000000000000000

11
data/oc1output/hepatitis.txt Executable file
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@@ -0,0 +1,11 @@
Remapping class numbers:
0 To 2
Pruned decision tree written to csv/hepatitis.csv.dt.
Unpruned decision tree written to csv/hepatitis.csv.dt.unpruned.
fold 1: acc. = 74.19 #leaves = 2 max. depth = 1
fold 2: acc. = 74.19 #leaves = 8 max. depth = 4
fold 3: acc. = 70.97 #leaves = 11 max. depth = 4
fold 4: acc. = 80.65 #leaves = 10 max. depth = 4
fold 5: acc. = 77.42 #leaves = 12 max. depth = 5
accuracy = 75.48 #leaves = 8.60 max depth = 3.60
.20400000000000000000

View File

@@ -0,0 +1,12 @@
Remapping class numbers:
0 To 1
1 To 2
Pruned decision tree written to csv/ilpd-indian-liver.csv.dt.
Unpruned decision tree written to csv/ilpd-indian-liver.csv.dt.unpruned.
fold 1: acc. = 71.55 #leaves = 10 max. depth = 7
fold 2: acc. = 70.69 #leaves = 49 max. depth = 12
fold 3: acc. = 68.10 #leaves = 46 max. depth = 12
fold 4: acc. = 66.38 #leaves = 50 max. depth = 13
fold 5: acc. = 64.71 #leaves = 53 max. depth = 13
accuracy = 68.27 #leaves = 41.60 max depth = 11.40
1.60900000000000000000

11
data/oc1output/ionosphere.txt Executable file
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@@ -0,0 +1,11 @@
Remapping class numbers:
0 To 2
Pruned decision tree written to csv/ionosphere.csv.dt.
Unpruned decision tree written to csv/ionosphere.csv.dt.unpruned.
fold 1: acc. = 87.14 #leaves = 3 max. depth = 2
fold 2: acc. = 81.43 #leaves = 17 max. depth = 7
fold 3: acc. = 84.29 #leaves = 14 max. depth = 7
fold 4: acc. = 92.86 #leaves = 9 max. depth = 5
fold 5: acc. = 92.96 #leaves = 12 max. depth = 8
accuracy = 87.75 #leaves = 11.00 max depth = 5.80
1.23400000000000000000

12
data/oc1output/iris.txt Executable file
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@@ -0,0 +1,12 @@
Remapping class numbers:
0 To 1
1 To 2
2 To 3
Unpruned decision tree written to csv/iris.csv.dt.
fold 1: acc. = 90.00 #leaves = 3 max. depth = 2
fold 2: acc. = 96.67 #leaves = 4 max. depth = 3
fold 3: acc. = 93.33 #leaves = 5 max. depth = 4
fold 4: acc. = 96.67 #leaves = 5 max. depth = 4
fold 5: acc. = 93.33 #leaves = 5 max. depth = 4
accuracy = 94.00 #leaves = 4.40 max depth = 3.40
.08200000000000000000

19
data/oc1output/led-display.txt Executable file
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@@ -0,0 +1,19 @@
Remapping class numbers:
7 To 1
5 To 3
0 To 4
4 To 5
9 To 6
8 To 7
6 To 8
3 To 9
1 To 10
Pruned decision tree written to csv/led-display.csv.dt.
Unpruned decision tree written to csv/led-display.csv.dt.unpruned.
fold 1: acc. = 66.50 #leaves = 14 max. depth = 6
fold 2: acc. = 74.50 #leaves = 61 max. depth = 9
fold 3: acc. = 68.00 #leaves = 61 max. depth = 8
fold 4: acc. = 63.50 #leaves = 62 max. depth = 8
fold 5: acc. = 67.50 #leaves = 59 max. depth = 8
accuracy = 68.00 #leaves = 51.40 max depth = 7.80
1.46800000000000000000

25
data/oc1output/libras.txt Executable file
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@@ -0,0 +1,25 @@
Remapping class numbers:
0 To 1
1 To 2
2 To 3
3 To 4
4 To 5
5 To 6
6 To 7
7 To 8
8 To 9
9 To 10
10 To 11
11 To 12
12 To 13
13 To 14
14 To 15
Pruned decision tree written to csv/libras.csv.dt.
Unpruned decision tree written to csv/libras.csv.dt.unpruned.
fold 1: acc. = 65.28 #leaves = 22 max. depth = 6
fold 2: acc. = 58.33 #leaves = 61 max. depth = 9
fold 3: acc. = 61.11 #leaves = 57 max. depth = 8
fold 4: acc. = 62.50 #leaves = 61 max. depth = 9
fold 5: acc. = 62.50 #leaves = 62 max. depth = 10
accuracy = 61.94 #leaves = 52.60 max depth = 8.40
2.56300000000000000000

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@@ -0,0 +1,18 @@
Remapping class numbers:
7 To 2
8 To 3
0 To 4
3 To 5
2 To 6
6 To 7
5 To 8
4 To 9
Pruned decision tree written to csv/low-res-spect.csv.dt.
Unpruned decision tree written to csv/low-res-spect.csv.dt.unpruned.
fold 1: acc. = 76.42 #leaves = 9 max. depth = 5
fold 2: acc. = 85.85 #leaves = 35 max. depth = 8
fold 3: acc. = 84.91 #leaves = 34 max. depth = 7
fold 4: acc. = 83.96 #leaves = 35 max. depth = 8
fold 5: acc. = 83.18 #leaves = 36 max. depth = 7
accuracy = 82.86 #leaves = 29.80 max depth = 7.00
5.99000000000000000000

13
data/oc1output/lymphography.txt Executable file
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@@ -0,0 +1,13 @@
Remapping class numbers:
2 To 1
1 To 2
0 To 4
Pruned decision tree written to csv/lymphography.csv.dt.
Unpruned decision tree written to csv/lymphography.csv.dt.unpruned.
fold 1: acc. = 75.86 #leaves = 4 max. depth = 3
fold 2: acc. = 68.97 #leaves = 11 max. depth = 6
fold 3: acc. = 79.31 #leaves = 10 max. depth = 4
fold 4: acc. = 75.86 #leaves = 11 max. depth = 5
fold 5: acc. = 65.62 #leaves = 15 max. depth = 5
accuracy = 72.97 #leaves = 10.20 max depth = 4.60
.21600000000000000000

11
data/oc1output/mammographic.txt Executable file
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@@ -0,0 +1,11 @@
Remapping class numbers:
0 To 2
Pruned decision tree written to csv/mammographic.csv.dt.
Unpruned decision tree written to csv/mammographic.csv.dt.unpruned.
fold 1: acc. = 83.85 #leaves = 2 max. depth = 1
fold 2: acc. = 73.44 #leaves = 106 max. depth = 19
fold 3: acc. = 75.00 #leaves = 104 max. depth = 21
fold 4: acc. = 78.65 #leaves = 110 max. depth = 13
fold 5: acc. = 72.54 #leaves = 98 max. depth = 14
accuracy = 76.69 #leaves = 84.00 max depth = 13.60
2.02400000000000000000

View File

@@ -0,0 +1,12 @@
Remapping class numbers:
0 To 1
1 To 2
Pruned decision tree written to csv/molec-biol-promoter.csv.dt.
Unpruned decision tree written to csv/molec-biol-promoter.csv.dt.unpruned.
fold 1: acc. = 66.67 #leaves = 4 max. depth = 3
fold 2: acc. = 61.90 #leaves = 13 max. depth = 5
fold 3: acc. = 71.43 #leaves = 11 max. depth = 5
fold 4: acc. = 66.67 #leaves = 10 max. depth = 5
fold 5: acc. = 68.18 #leaves = 11 max. depth = 4
accuracy = 66.98 #leaves = 9.80 max depth = 4.40
.01800000000000000000

11
data/oc1output/musk-1.txt Executable file
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@@ -0,0 +1,11 @@
Remapping class numbers:
0 To 2
Pruned decision tree written to csv/musk-1.csv.dt.
Unpruned decision tree written to csv/musk-1.csv.dt.unpruned.
fold 1: acc. = 80.00 #leaves = 14 max. depth = 11
fold 2: acc. = 83.16 #leaves = 29 max. depth = 9
fold 3: acc. = 80.00 #leaves = 30 max. depth = 11
fold 4: acc. = 81.05 #leaves = 34 max. depth = 14
fold 5: acc. = 80.21 #leaves = 34 max. depth = 16
accuracy = 80.88 #leaves = 28.20 max depth = 12.20
3.84800000000000000000

View File

@@ -0,0 +1,11 @@
Remapping class numbers:
0 To 2
Pruned decision tree written to csv/oocytes_merluccius_nucleus_4d.csv.dt.
Unpruned decision tree written to csv/oocytes_merluccius_nucleus_4d.csv.dt.unpruned.
fold 1: acc. = 83.82 #leaves = 38 max. depth = 11
fold 2: acc. = 74.02 #leaves = 81 max. depth = 12
fold 3: acc. = 71.57 #leaves = 84 max. depth = 13
fold 4: acc. = 73.53 #leaves = 88 max. depth = 12
fold 5: acc. = 68.45 #leaves = 85 max. depth = 13
accuracy = 74.27 #leaves = 75.20 max depth = 12.20
7.67300000000000000000

View File

@@ -0,0 +1,13 @@
Remapping class numbers:
2 To 1
1 To 2
0 To 3
Pruned decision tree written to csv/oocytes_merluccius_states_2f.csv.dt.
Unpruned decision tree written to csv/oocytes_merluccius_states_2f.csv.dt.unpruned.
fold 1: acc. = 88.24 #leaves = 5 max. depth = 3
fold 2: acc. = 87.75 #leaves = 35 max. depth = 9
fold 3: acc. = 90.69 #leaves = 41 max. depth = 9
fold 4: acc. = 87.25 #leaves = 35 max. depth = 12
fold 5: acc. = 85.92 #leaves = 34 max. depth = 11
accuracy = 87.96 #leaves = 30.00 max depth = 8.80
5.23000000000000000000

View File

@@ -0,0 +1,11 @@
Remapping class numbers:
0 To 2
Pruned decision tree written to csv/oocytes_trisopterus_nucleus_2f.csv.dt.
Unpruned decision tree written to csv/oocytes_trisopterus_nucleus_2f.csv.dt.unpruned.
fold 1: acc. = 75.82 #leaves = 17 max. depth = 8
fold 2: acc. = 73.63 #leaves = 55 max. depth = 12
fold 3: acc. = 70.33 #leaves = 70 max. depth = 15
fold 4: acc. = 70.33 #leaves = 59 max. depth = 10
fold 5: acc. = 74.46 #leaves = 70 max. depth = 11
accuracy = 72.92 #leaves = 54.20 max depth = 11.20
4.95800000000000000000

View File

@@ -0,0 +1,12 @@
Remapping class numbers:
0 To 2
2 To 3
Pruned decision tree written to csv/oocytes_trisopterus_states_5b.csv.dt.
Unpruned decision tree written to csv/oocytes_trisopterus_states_5b.csv.dt.unpruned.
fold 1: acc. = 86.81 #leaves = 7 max. depth = 4
fold 2: acc. = 84.62 #leaves = 45 max. depth = 19
fold 3: acc. = 84.07 #leaves = 41 max. depth = 12
fold 4: acc. = 93.96 #leaves = 48 max. depth = 16
fold 5: acc. = 81.52 #leaves = 42 max. depth = 12
accuracy = 86.18 #leaves = 36.60 max depth = 12.60
7.58400000000000000000

11
data/oc1output/parkinsons.txt Executable file
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@@ -0,0 +1,11 @@
Remapping class numbers:
0 To 2
Pruned decision tree written to csv/parkinsons.csv.dt.
Unpruned decision tree written to csv/parkinsons.csv.dt.unpruned.
fold 1: acc. = 89.74 #leaves = 4 max. depth = 3
fold 2: acc. = 92.31 #leaves = 7 max. depth = 4
fold 3: acc. = 84.62 #leaves = 8 max. depth = 6
fold 4: acc. = 76.92 #leaves = 8 max. depth = 6
fold 5: acc. = 89.74 #leaves = 7 max. depth = 5
accuracy = 86.67 #leaves = 6.80 max depth = 4.80
.47200000000000000000

11
data/oc1output/pima.txt Executable file
View File

@@ -0,0 +1,11 @@
Remapping class numbers:
0 To 2
Pruned decision tree written to csv/pima.csv.dt.
Unpruned decision tree written to csv/pima.csv.dt.unpruned.
fold 1: acc. = 71.90 #leaves = 6 max. depth = 5
fold 2: acc. = 70.59 #leaves = 70 max. depth = 11
fold 3: acc. = 70.59 #leaves = 61 max. depth = 12
fold 4: acc. = 71.24 #leaves = 67 max. depth = 11
fold 5: acc. = 66.67 #leaves = 60 max. depth = 13
accuracy = 70.18 #leaves = 52.80 max depth = 10.40
1.88100000000000000000

View File

@@ -0,0 +1,13 @@
Remapping class numbers:
0 To 1
1 To 2
2 To 3
Pruned decision tree written to csv/pittsburg-bridges-MATERIAL.csv.dt.
Unpruned decision tree written to csv/pittsburg-bridges-MATERIAL.csv.dt.unpruned.
fold 1: acc. = 71.43 #leaves = 3 max. depth = 2
fold 2: acc. = 61.90 #leaves = 8 max. depth = 5
fold 3: acc. = 80.95 #leaves = 8 max. depth = 4
fold 4: acc. = 85.71 #leaves = 12 max. depth = 6
fold 5: acc. = 95.45 #leaves = 9 max. depth = 5
accuracy = 79.25 #leaves = 8.00 max depth = 4.40
.07500000000000000000

View File

@@ -0,0 +1,13 @@
Remapping class numbers:
0 To 1
1 To 2
2 To 3
Pruned decision tree written to csv/pittsburg-bridges-REL-L.csv.dt.
Unpruned decision tree written to csv/pittsburg-bridges-REL-L.csv.dt.unpruned.
fold 1: acc. = 70.00 #leaves = 3 max. depth = 2
fold 2: acc. = 70.00 #leaves = 17 max. depth = 8
fold 3: acc. = 70.00 #leaves = 16 max. depth = 7
fold 4: acc. = 70.00 #leaves = 12 max. depth = 4
fold 5: acc. = 60.87 #leaves = 14 max. depth = 7
accuracy = 67.96 #leaves = 12.40 max depth = 5.60
.12200000000000000000

View File

@@ -0,0 +1,13 @@
Remapping class numbers:
0 To 1
1 To 2
2 To 3
Pruned decision tree written to csv/pittsburg-bridges-SPAN.csv.dt.
Unpruned decision tree written to csv/pittsburg-bridges-SPAN.csv.dt.unpruned.
fold 1: acc. = 55.56 #leaves = 2 max. depth = 1
fold 2: acc. = 72.22 #leaves = 19 max. depth = 8
fold 3: acc. = 61.11 #leaves = 15 max. depth = 7
fold 4: acc. = 50.00 #leaves = 9 max. depth = 5
fold 5: acc. = 70.00 #leaves = 11 max. depth = 6
accuracy = 61.96 #leaves = 11.20 max depth = 5.40
.10200000000000000000

View File

@@ -0,0 +1,12 @@
Remapping class numbers:
0 To 1
1 To 2
Pruned decision tree written to csv/pittsburg-bridges-T-OR-D.csv.dt.
Unpruned decision tree written to csv/pittsburg-bridges-T-OR-D.csv.dt.unpruned.
fold 1: acc. = 65.00 #leaves = 2 max. depth = 1
fold 2: acc. = 75.00 #leaves = 9 max. depth = 6
fold 3: acc. = 85.00 #leaves = 8 max. depth = 5
fold 4: acc. = 85.00 #leaves = 9 max. depth = 6
fold 5: acc. = 81.82 #leaves = 9 max. depth = 5
accuracy = 78.43 #leaves = 7.40 max depth = 4.60
.08400000000000000000

12
data/oc1output/planning.txt Executable file
View File

@@ -0,0 +1,12 @@
Remapping class numbers:
0 To 1
1 To 2
Pruned decision tree written to csv/planning.csv.dt.
Unpruned decision tree written to csv/planning.csv.dt.unpruned.
fold 1: acc. = 75.00 #leaves = 2 max. depth = 1
fold 2: acc. = 61.11 #leaves = 20 max. depth = 9
fold 3: acc. = 58.33 #leaves = 23 max. depth = 8
fold 4: acc. = 63.89 #leaves = 22 max. depth = 7
fold 5: acc. = 55.26 #leaves = 22 max. depth = 8
accuracy = 62.64 #leaves = 17.80 max depth = 6.60
.35900000000000000000

View File

@@ -0,0 +1,12 @@
Remapping class numbers:
0 To 1
1 To 3
Pruned decision tree written to csv/post-operative.csv.dt.
Unpruned decision tree written to csv/post-operative.csv.dt.unpruned.
fold 1: acc. = 72.22 #leaves = 8 max. depth = 4
fold 2: acc. = 50.00 #leaves = 20 max. depth = 7
fold 3: acc. = 55.56 #leaves = 17 max. depth = 9
fold 4: acc. = 55.56 #leaves = 13 max. depth = 5
fold 5: acc. = 61.11 #leaves = 16 max. depth = 8
accuracy = 58.89 #leaves = 14.80 max depth = 6.60
.09100000000000000000

13
data/oc1output/seeds.txt Executable file
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@@ -0,0 +1,13 @@
Remapping class numbers:
0 To 1
1 To 2
2 To 3
Pruned decision tree written to csv/seeds.csv.dt.
Unpruned decision tree written to csv/seeds.csv.dt.unpruned.
fold 1: acc. = 95.24 #leaves = 3 max. depth = 2
fold 2: acc. = 95.24 #leaves = 6 max. depth = 5
fold 3: acc. = 92.86 #leaves = 4 max. depth = 3
fold 4: acc. = 92.86 #leaves = 7 max. depth = 5
fold 5: acc. = 92.86 #leaves = 6 max. depth = 3
accuracy = 93.81 #leaves = 5.20 max depth = 3.60
.17400000000000000000

View File

@@ -0,0 +1,11 @@
Remapping class numbers:
0 To 2
Pruned decision tree written to csv/statlog-australian-credit.csv.dt.
Unpruned decision tree written to csv/statlog-australian-credit.csv.dt.unpruned.
fold 1: acc. = 60.14 #leaves = 3 max. depth = 2
fold 2: acc. = 59.42 #leaves = 64 max. depth = 12
fold 3: acc. = 57.97 #leaves = 61 max. depth = 12
fold 4: acc. = 59.42 #leaves = 66 max. depth = 13
fold 5: acc. = 59.42 #leaves = 58 max. depth = 13
accuracy = 59.28 #leaves = 50.40 max depth = 10.40
2.20300000000000000000

View File

@@ -0,0 +1,12 @@
Remapping class numbers:
0 To 1
1 To 2
Pruned decision tree written to csv/statlog-german-credit.csv.dt.
Unpruned decision tree written to csv/statlog-german-credit.csv.dt.unpruned.
fold 1: acc. = 70.50 #leaves = 2 max. depth = 1
fold 2: acc. = 65.00 #leaves = 81 max. depth = 14
fold 3: acc. = 71.50 #leaves = 69 max. depth = 12
fold 4: acc. = 67.00 #leaves = 67 max. depth = 12
fold 5: acc. = 61.50 #leaves = 64 max. depth = 14
accuracy = 67.10 #leaves = 56.60 max depth = 10.60
4.37200000000000000000

View File

@@ -0,0 +1,10 @@
Remapping class numbers:
0 To 2
Unpruned decision tree written to csv/statlog-heart.csv.dt.
fold 1: acc. = 74.07 #leaves = 13 max. depth = 7
fold 2: acc. = 81.48 #leaves = 15 max. depth = 7
fold 3: acc. = 68.52 #leaves = 15 max. depth = 9
fold 4: acc. = 74.07 #leaves = 16 max. depth = 6
fold 5: acc. = 61.11 #leaves = 14 max. depth = 6
accuracy = 71.85 #leaves = 14.60 max depth = 7.00
.43200000000000000000

View File

@@ -0,0 +1,16 @@
Remapping class numbers:
5 To 1
1 To 3
6 To 4
0 To 5
3 To 6
4 To 7
Pruned decision tree written to csv/statlog-image.csv.dt.
Unpruned decision tree written to csv/statlog-image.csv.dt.unpruned.
fold 1: acc. = 94.16 #leaves = 38 max. depth = 8
fold 2: acc. = 96.32 #leaves = 40 max. depth = 12
fold 3: acc. = 92.86 #leaves = 49 max. depth = 10
fold 4: acc. = 96.54 #leaves = 44 max. depth = 11
fold 5: acc. = 92.21 #leaves = 44 max. depth = 9
accuracy = 94.42 #leaves = 43.00 max depth = 10.00
11.76100000000000000000

View File

@@ -0,0 +1,12 @@
Remapping class numbers:
3 To 1
0 To 3
1 To 4
Unpruned decision tree written to csv/statlog-vehicle.csv.dt.
fold 1: acc. = 71.01 #leaves = 75 max. depth = 13
fold 2: acc. = 77.51 #leaves = 81 max. depth = 15
fold 3: acc. = 66.86 #leaves = 76 max. depth = 12
fold 4: acc. = 74.56 #leaves = 76 max. depth = 13
fold 5: acc. = 68.82 #leaves = 65 max. depth = 13
accuracy = 71.75 #leaves = 74.60 max depth = 13.20
4.58300000000000000000

View File

@@ -0,0 +1,16 @@
Remapping class numbers:
0 To 1
1 To 2
2 To 3
3 To 4
4 To 5
5 To 6
Pruned decision tree written to csv/synthetic-control.csv.dt.
Unpruned decision tree written to csv/synthetic-control.csv.dt.unpruned.
fold 1: acc. = 82.50 #leaves = 11 max. depth = 6
fold 2: acc. = 85.83 #leaves = 21 max. depth = 7
fold 3: acc. = 83.33 #leaves = 21 max. depth = 6
fold 4: acc. = 82.50 #leaves = 24 max. depth = 6
fold 5: acc. = 81.67 #leaves = 25 max. depth = 7
accuracy = 83.17 #leaves = 20.40 max depth = 6.40
7.25500000000000000000

11
data/oc1output/tic-tac-toe.txt Executable file
View File

@@ -0,0 +1,11 @@
Remapping class numbers:
0 To 2
Pruned decision tree written to csv/tic-tac-toe.csv.dt.
Unpruned decision tree written to csv/tic-tac-toe.csv.dt.unpruned.
fold 1: acc. = 91.62 #leaves = 11 max. depth = 5
fold 2: acc. = 91.62 #leaves = 21 max. depth = 8
fold 3: acc. = 89.01 #leaves = 28 max. depth = 11
fold 4: acc. = 94.24 #leaves = 25 max. depth = 9
fold 5: acc. = 94.33 #leaves = 21 max. depth = 8
accuracy = 92.17 #leaves = 21.20 max depth = 8.20
1.75200000000000000000

View File

@@ -0,0 +1,12 @@
Remapping class numbers:
0 To 1
1 To 2
Pruned decision tree written to csv/vertebral-column-2clases.csv.dt.
Unpruned decision tree written to csv/vertebral-column-2clases.csv.dt.unpruned.
fold 1: acc. = 87.10 #leaves = 5 max. depth = 4
fold 2: acc. = 72.58 #leaves = 19 max. depth = 7
fold 3: acc. = 79.03 #leaves = 21 max. depth = 8
fold 4: acc. = 80.65 #leaves = 18 max. depth = 7
fold 5: acc. = 82.26 #leaves = 19 max. depth = 7
accuracy = 80.32 #leaves = 16.40 max depth = 6.60
.38200000000000000000

13
data/oc1output/wine.txt Executable file
View File

@@ -0,0 +1,13 @@
Remapping class numbers:
0 To 1
1 To 2
2 To 3
Pruned decision tree written to csv/wine.csv.dt.
Unpruned decision tree written to csv/wine.csv.dt.unpruned.
fold 1: acc. = 85.71 #leaves = 3 max. depth = 2
fold 2: acc. = 94.29 #leaves = 4 max. depth = 3
fold 3: acc. = 97.14 #leaves = 4 max. depth = 3
fold 4: acc. = 94.29 #leaves = 4 max. depth = 3
fold 5: acc. = 92.11 #leaves = 4 max. depth = 3
accuracy = 92.70 #leaves = 3.80 max depth = 2.80
.17000000000000000000

16
data/oc1output/zoo.txt Executable file
View File

@@ -0,0 +1,16 @@
Remapping class numbers:
0 To 1
3 To 2
1 To 3
6 To 4
4 To 6
2 To 7
Pruned decision tree written to csv/zoo.csv.dt.
Unpruned decision tree written to csv/zoo.csv.dt.unpruned.
fold 1: acc. = 85.00 #leaves = 5 max. depth = 4
fold 2: acc. = 80.00 #leaves = 9 max. depth = 6
fold 3: acc. = 85.00 #leaves = 7 max. depth = 4
fold 4: acc. = 90.00 #leaves = 10 max. depth = 7
fold 5: acc. = 85.71 #leaves = 10 max. depth = 7
accuracy = 85.15 #leaves = 8.20 max depth = 5.60
.07500000000000000000

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