Decision Tree Regression on the Diabetes Dataset Using the scikit Library

I had been working on a decision tree regression system, implemented from scratch, using the C# language. In order to validate from from-scratch implementation, I figured I’d run some data through my from-scratch implementation and the scikit DecisionTreeRegressor module to make sure I got the same results.

For my investigation, I decided to use the well-known diabetes dataset. The goal of the dataset is to predict a diabetes score from 10 predictor variables. The data looks like:

59, 2, 32.1, 101.00, 157, 93.2, 38, 4.00, 4.8598, 87, 151
48, 1, 21.6, 87.00, 183, 103.2, 70, 3.00, 3.8918, 69, 75
. . .

The 10 predictor variables are age, sex, body mass index, blood pressure, serum-cholesterol, low-density lipoproteins, high-density lipoproteins, total-cholesterol, triglycerides, blood-sugar. The target diabetes score is in last column.

There are 442 items in the diabetes dataset. I split the data into a 342-item training set and a 100-item test set. I did not normalize the data.

The output of the scikit demo is:

Decision tree regression using scikit

Loading diabetes train (340), test (100) data
Done

First three X predictors:
[ 59.0000   2.0000  32.1000 . . .   87.0000]
[ 48.0000   1.0000  21.6000  . . .  69.0000]
[ 72.0000   2.0000  30.5000  . . .  85.0000]

First three y targets:
151.0000
75.0000
141.0000

Setting max_depth = 12

Accuracy on train (within 0.10) = 0.8304
Accuracy on test (within 0.10) = 0.1200

MSE on train = 190.5995
MSE on test = 6936.7650

Predicting for:
[[ 59.0000   2.0000  32.1000 . . .  87.0000]]
Predicted y = 151.0000

Done

I set the DecisionTreeRegressor max-depth parameter to 12, and left all other parameters with default values, in particular min_samples_split=2 and min_samples_leaf=1.

The results show the key weakness of decision tree regression: the model almost always overfits the training data and therefore gives poor prediction accuracy on new, previously unseen data. This is why decision tree regression is rarely used by itself — instead many decision trees are used together in common ensemble techniques: bagging tree (bootstrap aggregation), random forest tree, adaptive boosting, gradient boosting.

The output of my from-scratch C# implementation was almost the same:

Begin decision tree regression (no stack construction)

Loading diabetes train (342) and test (100) data
Done

First three train X:
 59.0000  2.0000 32.1000 . . . 87.0000
 48.0000  1.0000 21.6000 . . . 69.0000
 72.0000  2.0000 30.5000 . . . 85.0000

First three train y:
151.0000
 75.0000
141.0000

Setting maxDepth = 12
Setting minSamples = 2
Setting minLeaf = 1
Using default numSplitCols = -1

Creating and training tree
Done

Evaluating model
Accuracy train (within 0.10) = 0.8304
Accuracy test (within 0.10) = 0.1300

MSE train = 190.5995
MSE test = 6936.0606

Predicting for trainX[0] =
  59.0000   2.0000  32.1000 . . . 87.0000
Predicted y = 151.0000

IF
column 8  >    4.8203 AND
column 3  <= 112.0000 AND
column 9  <=  98.0000 AND
column 2  <=  32.1000 AND
column 3  >   83.0000 AND
column 5  >   84.2000 AND
column 2  >   30.0000 AND
column 0  >   40.0000 AND
column 8  <=   5.2470 AND
column 6  <=  38.0000 AND
THEN
predicted = 151.0000

End demo

The two systems (scikit version and C# version) don't give identical results because they use slightly different algorithms for splitting subtrees when constructing the decision tree.

Good fun.

Each node in a decision tree has a left child and a right child. Tree children are neither sons not daughters.

I am a huge fan of 1950s science fiction movies. A surprising number of these movies feature a daughter or granddaughter who plays a key role. Here are three examples, all from 1957 (a good year for science fiction movies).

Left: In "20 Million Miles to Earth" (1957), a secret U.S. mission to Venus returns to Earth but crash lands off the coast of Italy. The spacecraft has a small egg that is discovered by scientist Dr. Leonardo. Leonardo's granddaughter Marisa (actress Joan Taylor) is a medical student who is called to take care of the injured spacemen. The egg hatches and a lizard like creature emerges, and soo grows to an enormous size. A pretty good movie. I grade it my personal solid B.

Center: In "The Brain from Planet Arous" (1957), as you might expect, an evil alien brain creature from the planet Arous lands on Earth. The alien has mind control powers and takes over the body of scientist Steve March. Steve's fiancee Sally (actress Joyce Meadows) is the daughter of older scientist John Fallon. The brain is defeated. This movie is known for the creative floating brain alien with glowing eyes. I give it a B- grade.

Right: In "Monster from Green Hell" (1957), the U.S. space program is sending animals and insects into space. One rocket that is carrying wasps malfunctions and crash lands in Africa. Wasps + cosmic radiation = giant wasps. Scientist Dr. Lorentz and his daughter Lorna (actress Barbara Turner) are in the area and help U.S. scientist Dr. Quent Brady to try and deal with the wasps. Luckily, a well-timed volcanic eruption solves the wasp problem.

Demo code. Replace the "lt" with the Boolean less-than operator.

# decision_tree_scikit.py

import numpy as np
from sklearn.datasets import load_diabetes
from sklearn.tree import DecisionTreeRegressor

np.set_printoptions(precision=4, suppress=True,
  floatmode='fixed', linewidth=120)

# -----------------------------------------------------------

def accuracy(model, data_X, data_y, pct_close):
  n = len(data_X)
  n_correct = 0; n_wrong = 0
  for i in range(n):
    x = data_X[i].reshape(1,-1)
    y = data_y[i]
    y_pred = model.predict(x)

    if np.abs(y - y_pred) "lt" np.abs(y * pct_close):
      n_correct += 1
    else: 
      n_wrong += 1
  # print("Correct = " + str(n_correct))
  # print("Wrong   = " + str(n_wrong))
  return n_correct / (n_correct + n_wrong)

# -----------------------------------------------------------

def MSE(model, data_X, data_y):
  n = len(data_X)
  sum = 0.0
  for i in range(n):
    x = data_X[i].reshape(1,-1)
    y = data_y[i]
    y_pred = model.predict(x)[0]
    # print(y_pred); input()
    sum += (y - y_pred) * (y - y_pred)

  return sum / n

# -----------------------------------------------------------

# class sklearn.tree.DecisionTreeRegressor(*,
# criterion='squared_error', splitter='best',
# max_depth=None, min_samples_split=2,
# min_samples_leaf=1, min_weight_fraction_leaf=0.0,
# max_features=None, random_state=None,
# max_leaf_nodes=None, min_impurity_decrease=0.0,
# ccp_alpha=0.0, monotonic_cst=None

print("\nDecision tree regression using scikit ")

print("\nLoading diabetes train (342), test (100) data ")
train_file = ".\\Data\\diabetes_train_342.txt"
train_X = np.loadtxt(train_file, comments="#",
  usecols=[0,1,2,3,4,5,6,7,8,9],
  delimiter=",",  dtype=np.float64)
train_y = np.loadtxt(train_file, comments="#", usecols=10,
  delimiter=",",  dtype=np.float64)

test_file = ".\\Data\\diabetes_test_100.txt"
test_X = np.loadtxt(test_file, comments="#",
  usecols=[0,1,2,3,4,5,6,7,8,9],
  delimiter=",",  dtype=np.float64)
test_y = np.loadtxt(test_file, comments="#", usecols=10,
  delimiter=",",  dtype=np.float64)
print("Done ")

print("\nFirst three X predictors: ")
for i in range(3):
  print(train_X[i])
print("\nFirst three y targets: ")
for i in range(3):
  print("%0.4f" % train_y[i])

maxd = 12
print("\nSetting max_depth = " + str(maxd))
model = DecisionTreeRegressor(max_depth=maxd,
  random_state=0)
model.fit(train_X, train_y)

acc_train = accuracy(model, train_X, train_y, 0.10)
print("\nAccuracy on train (within 0.10) = \
%0.4f " % acc_train)
acc_test = accuracy(model, test_X, test_y, 0.10)
print("Accuracy on test (within 0.10) = \
%0.4f " % acc_test)

mse_train = MSE(model, train_X, train_y)
print("\nMSE on train = %0.4f " % mse_train)
mse_test = MSE(model, test_X, test_y)
print("MSE on test = %0.4f " % mse_test)

x = train_X[0].reshape(1,-1)
print("\nPredicting for: ")
print(x)
y_pred = model.predict(x)[0]
print("Predicted y = %0.4f " % y_pred)

print("\nDone ")

Training data:

# diabetes_train_342.txt
# 10 predictors, target y
# 342 items
# www4.stat.ncsu.edu/~boos/var.select/diabetes.tab.txt
#
59, 2, 32.1, 101.00, 157, 93.2, 38, 4.00, 4.8598, 87, 151
48, 1, 21.6, 87.00, 183, 103.2, 70, 3.00, 3.8918, 69, 75
72, 2, 30.5, 93.00, 156, 93.6, 41, 4.00, 4.6728, 85, 141
24, 1, 25.3, 84.00, 198, 131.4, 40, 5.00, 4.8903, 89, 206
50, 1, 23.0, 101.00, 192, 125.4, 52, 4.00, 4.2905, 80, 135
23, 1, 22.6, 89.00, 139, 64.8, 61, 2.00, 4.1897, 68, 97
36, 2, 22.0, 90.00, 160, 99.6, 50, 3.00, 3.9512, 82, 138
66, 2, 26.2, 114.00, 255, 185.0, 56, 4.55, 4.2485, 92, 63
60, 2, 32.1, 83.00, 179, 119.4, 42, 4.00, 4.4773, 94, 110
29, 1, 30.0, 85.00, 180, 93.4, 43, 4.00, 5.3845, 88, 310
22, 1, 18.6, 97.00, 114, 57.6, 46, 2.00, 3.9512, 83, 101
56, 2, 28.0, 85.00, 184, 144.8, 32, 6.00, 3.5835, 77, 69
53, 1, 23.7, 92.00, 186, 109.2, 62, 3.00, 4.3041, 81, 179
50, 2, 26.2, 97.00, 186, 105.4, 49, 4.00, 5.0626, 88, 185
61, 1, 24.0, 91.00, 202, 115.4, 72, 3.00, 4.2905, 73, 118
34, 2, 24.7, 118.00, 254, 184.2, 39, 7.00, 5.0370, 81, 171
47, 1, 30.3, 109.00, 207, 100.2, 70, 3.00, 5.2149, 98, 166
68, 2, 27.5, 111.00, 214, 147.0, 39, 5.00, 4.9416, 91, 144
38, 1, 25.4, 84.00, 162, 103.0, 42, 4.00, 4.4427, 87, 97
41, 1, 24.7, 83.00, 187, 108.2, 60, 3.00, 4.5433, 78, 168
35, 1, 21.1, 82.00, 156, 87.8, 50, 3.00, 4.5109, 95, 68
25, 2, 24.3, 95.00, 162, 98.6, 54, 3.00, 3.8501, 87, 49
25, 1, 26.0, 92.00, 187, 120.4, 56, 3.00, 3.9703, 88, 68
61, 2, 32.0, 103.67, 210, 85.2, 35, 6.00, 6.1070, 124, 245
31, 1, 29.7, 88.00, 167, 103.4, 48, 4.00, 4.3567, 78, 184
30, 2, 25.2, 83.00, 178, 118.4, 34, 5.00, 4.8520, 83, 202
19, 1, 19.2, 87.00, 124, 54.0, 57, 2.00, 4.1744, 90, 137
42, 1, 31.9, 83.00, 158, 87.6, 53, 3.00, 4.4659, 101, 85
63, 1, 24.4, 73.00, 160, 91.4, 48, 3.00, 4.6347, 78, 131
67, 2, 25.8, 113.00, 158, 54.2, 64, 2.00, 5.2933, 104, 283
32, 1, 30.5, 89.00, 182, 110.6, 56, 3.00, 4.3438, 89, 129
42, 1, 20.3, 71.00, 161, 81.2, 66, 2.00, 4.2341, 81, 59
58, 2, 38.0, 103.00, 150, 107.2, 22, 7.00, 4.6444, 98, 341
57, 1, 21.7, 94.00, 157, 58.0, 82, 2.00, 4.4427, 92, 87
53, 1, 20.5, 78.00, 147, 84.2, 52, 3.00, 3.9890, 75, 65
62, 2, 23.5, 80.33, 225, 112.8, 86, 2.62, 4.8752, 96, 102
52, 1, 28.5, 110.00, 195, 97.2, 60, 3.00, 5.2417, 85, 265
46, 1, 27.4, 78.00, 171, 88.0, 58, 3.00, 4.8283, 90, 276
48, 2, 33.0, 123.00, 253, 163.6, 44, 6.00, 5.4250, 97, 252
48, 2, 27.7, 73.00, 191, 119.4, 46, 4.00, 4.8520, 92, 90
50, 2, 25.6, 101.00, 229, 162.2, 43, 5.00, 4.7791, 114, 100
21, 1, 20.1, 63.00, 135, 69.0, 54, 3.00, 4.0943, 89, 55
32, 2, 25.4, 90.33, 153, 100.4, 34, 4.50, 4.5326, 83, 61
54, 1, 24.2, 74.00, 204, 109.0, 82, 2.00, 4.1744, 109, 92
61, 2, 32.7, 97.00, 177, 118.4, 29, 6.00, 4.9972, 87, 259
56, 2, 23.1, 104.00, 181, 116.4, 47, 4.00, 4.4773, 79, 53
33, 1, 25.3, 85.00, 155, 85.0, 51, 3.00, 4.5539, 70, 190
27, 1, 19.6, 78.00, 128, 68.0, 43, 3.00, 4.4427, 71, 142
67, 2, 22.5, 98.00, 191, 119.2, 61, 3.00, 3.9890, 86, 75
37, 2, 27.7, 93.00, 180, 119.4, 30, 6.00, 5.0304, 88, 142
58, 1, 25.7, 99.00, 157, 91.6, 49, 3.00, 4.4067, 93, 155
65, 2, 27.9, 103.00, 159, 96.8, 42, 4.00, 4.6151, 86, 225
34, 1, 25.5, 93.00, 218, 144.0, 57, 4.00, 4.4427, 88, 59
46, 1, 24.9, 115.00, 198, 129.6, 54, 4.00, 4.2767, 103, 104
35, 1, 28.7, 97.00, 204, 126.8, 64, 3.00, 4.1897, 93, 182
37, 1, 21.8, 84.00, 184, 101.0, 73, 3.00, 3.9120, 93, 128
37, 1, 30.2, 87.00, 166, 96.0, 40, 4.15, 5.0106, 87, 52
41, 1, 20.5, 80.00, 124, 48.8, 64, 2.00, 4.0254, 75, 37
60, 1, 20.4, 105.00, 198, 78.4, 99, 2.00, 4.6347, 79, 170
66, 2, 24.0, 98.00, 236, 146.4, 58, 4.00, 5.0626, 96, 170
29, 1, 26.0, 83.00, 141, 65.2, 64, 2.00, 4.0775, 83, 61
37, 2, 26.8, 79.00, 157, 98.0, 28, 6.00, 5.0434, 96, 144
41, 2, 25.7, 83.00, 181, 106.6, 66, 3.00, 3.7377, 85, 52
39, 1, 22.9, 77.00, 204, 143.2, 46, 4.00, 4.3041, 74, 128
67, 2, 24.0, 83.00, 143, 77.2, 49, 3.00, 4.4308, 94, 71
36, 2, 24.1, 112.00, 193, 125.0, 35, 6.00, 5.1059, 95, 163
46, 2, 24.7, 85.00, 174, 123.2, 30, 6.00, 4.6444, 96, 150
60, 2, 25.0, 89.67, 185, 120.8, 46, 4.02, 4.5109, 92, 97
59, 2, 23.6, 83.00, 165, 100.0, 47, 4.00, 4.4998, 92, 160
53, 1, 22.1, 93.00, 134, 76.2, 46, 3.00, 4.0775, 96, 178
48, 1, 19.9, 91.00, 189, 109.6, 69, 3.00, 3.9512, 101, 48
48, 1, 29.5, 131.00, 207, 132.2, 47, 4.00, 4.9345, 106, 270
66, 2, 26.0, 91.00, 264, 146.6, 65, 4.00, 5.5683, 87, 202
52, 2, 24.5, 94.00, 217, 149.4, 48, 5.00, 4.5850, 89, 111
52, 2, 26.6, 111.00, 209, 126.4, 61, 3.00, 4.6821, 109, 85
46, 2, 23.5, 87.00, 181, 114.8, 44, 4.00, 4.7095, 98, 42
40, 2, 29.0, 115.00, 97, 47.2, 35, 2.77, 4.3041, 95, 170
22, 1, 23.0, 73.00, 161, 97.8, 54, 3.00, 3.8286, 91, 200
50, 1, 21.0, 88.00, 140, 71.8, 35, 4.00, 5.1120, 71, 252
20, 1, 22.9, 87.00, 191, 128.2, 53, 4.00, 3.8918, 85, 113
68, 1, 27.5, 107.00, 241, 149.6, 64, 4.00, 4.9200, 90, 143
52, 2, 24.3, 86.00, 197, 133.6, 44, 5.00, 4.5747, 91, 51
44, 1, 23.1, 87.00, 213, 126.4, 77, 3.00, 3.8712, 72, 52
38, 1, 27.3, 81.00, 146, 81.6, 47, 3.00, 4.4659, 81, 210
49, 1, 22.7, 65.33, 168, 96.2, 62, 2.71, 3.8918, 60, 65
61, 1, 33.0, 95.00, 182, 114.8, 54, 3.00, 4.1897, 74, 141
29, 2, 19.4, 83.00, 152, 105.8, 39, 4.00, 3.5835, 83, 55
61, 1, 25.8, 98.00, 235, 125.8, 76, 3.00, 5.1120, 82, 134
34, 2, 22.6, 75.00, 166, 91.8, 60, 3.00, 4.2627, 108, 42
36, 1, 21.9, 89.00, 189, 105.2, 68, 3.00, 4.3694, 96, 111
52, 1, 24.0, 83.00, 167, 86.6, 71, 2.00, 3.8501, 94, 98
61, 1, 31.2, 79.00, 235, 156.8, 47, 5.00, 5.0499, 96, 164
43, 1, 26.8, 123.00, 193, 102.2, 67, 3.00, 4.7791, 94, 48
35, 1, 20.4, 65.00, 187, 105.6, 67, 2.79, 4.2767, 78, 96
27, 1, 24.8, 91.00, 189, 106.8, 69, 3.00, 4.1897, 69, 90
29, 1, 21.0, 71.00, 156, 97.0, 38, 4.00, 4.6540, 90, 162
64, 2, 27.3, 109.00, 186, 107.6, 38, 5.00, 5.3083, 99, 150
41, 1, 34.6, 87.33, 205, 142.6, 41, 5.00, 4.6728, 110, 279
49, 2, 25.9, 91.00, 178, 106.6, 52, 3.00, 4.5747, 75, 92
48, 1, 20.4, 98.00, 209, 139.4, 46, 5.00, 4.7707, 78, 83
53, 1, 28.0, 88.00, 233, 143.8, 58, 4.00, 5.0499, 91, 128
53, 2, 22.2, 113.00, 197, 115.2, 67, 3.00, 4.3041, 100, 102
23, 1, 29.0, 90.00, 216, 131.4, 65, 3.00, 4.5850, 91, 302
65, 2, 30.2, 98.00, 219, 160.6, 40, 5.00, 4.5218, 84, 198
41, 1, 32.4, 94.00, 171, 104.4, 56, 3.00, 3.9703, 76, 95
55, 2, 23.4, 83.00, 166, 101.6, 46, 4.00, 4.5218, 96, 53
22, 1, 19.3, 82.00, 156, 93.2, 52, 3.00, 3.9890, 71, 134
56, 1, 31.0, 78.67, 187, 141.4, 34, 5.50, 4.0604, 90, 144
54, 2, 30.6, 103.33, 144, 79.8, 30, 4.80, 5.1417, 101, 232
59, 2, 25.5, 95.33, 190, 139.4, 35, 5.43, 4.3567, 117, 81
60, 2, 23.4, 88.00, 153, 89.8, 58, 3.00, 3.2581, 95, 104
54, 1, 26.8, 87.00, 206, 122.0, 68, 3.00, 4.3820, 80, 59
25, 1, 28.3, 87.00, 193, 128.0, 49, 4.00, 4.3820, 92, 246
54, 2, 27.7, 113.00, 200, 128.4, 37, 5.00, 5.1533, 113, 297
55, 1, 36.6, 113.00, 199, 94.4, 43, 4.63, 5.7301, 97, 258
40, 2, 26.5, 93.00, 236, 147.0, 37, 7.00, 5.5607, 92, 229
62, 2, 31.8, 115.00, 199, 128.6, 44, 5.00, 4.8828, 98, 275
65, 1, 24.4, 120.00, 222, 135.6, 37, 6.00, 5.5094, 124, 281
33, 2, 25.4, 102.00, 206, 141.0, 39, 5.00, 4.8675, 105, 179
53, 1, 22.0, 94.00, 175, 88.0, 59, 3.00, 4.9416, 98, 200
35, 1, 26.8, 98.00, 162, 103.6, 45, 4.00, 4.2047, 86, 200
66, 1, 28.0, 101.00, 195, 129.2, 40, 5.00, 4.8598, 94, 173
62, 2, 33.9, 101.00, 221, 156.4, 35, 6.00, 4.9972, 103, 180
50, 2, 29.6, 94.33, 300, 242.4, 33, 9.09, 4.8122, 109, 84
47, 1, 28.6, 97.00, 164, 90.6, 56, 3.00, 4.4659, 88, 121
47, 2, 25.6, 94.00, 165, 74.8, 40, 4.00, 5.5255, 93, 161
24, 1, 20.7, 87.00, 149, 80.6, 61, 2.00, 3.6109, 78, 99
58, 2, 26.2, 91.00, 217, 124.2, 71, 3.00, 4.6913, 68, 109
34, 1, 20.6, 87.00, 185, 112.2, 58, 3.00, 4.3041, 74, 115
51, 1, 27.9, 96.00, 196, 122.2, 42, 5.00, 5.0689, 120, 268
31, 2, 35.3, 125.00, 187, 112.4, 48, 4.00, 4.8903, 109, 274
22, 1, 19.9, 75.00, 175, 108.6, 54, 3.00, 4.1271, 72, 158
53, 2, 24.4, 92.00, 214, 146.0, 50, 4.00, 4.4998, 97, 107
37, 2, 21.4, 83.00, 128, 69.6, 49, 3.00, 3.8501, 84, 83
28, 1, 30.4, 85.00, 198, 115.6, 67, 3.00, 4.3438, 80, 103
47, 1, 31.6, 84.00, 154, 88.0, 30, 5.10, 5.1985, 105, 272
23, 1, 18.8, 78.00, 145, 72.0, 63, 2.00, 3.9120, 86, 85
50, 1, 31.0, 123.00, 178, 105.0, 48, 4.00, 4.8283, 88, 280
58, 2, 36.7, 117.00, 166, 93.8, 44, 4.00, 4.9488, 109, 336
55, 1, 32.1, 110.00, 164, 84.2, 42, 4.00, 5.2417, 90, 281
60, 2, 27.7, 107.00, 167, 114.6, 38, 4.00, 4.2767, 95, 118
41, 1, 30.8, 81.00, 214, 152.0, 28, 7.60, 5.1358, 123, 317
60, 2, 27.5, 106.00, 229, 143.8, 51, 4.00, 5.1417, 91, 235
40, 1, 26.9, 92.00, 203, 119.8, 70, 3.00, 4.1897, 81, 60
57, 2, 30.7, 90.00, 204, 147.8, 34, 6.00, 4.7095, 93, 174
37, 1, 38.3, 113.00, 165, 94.6, 53, 3.00, 4.4659, 79, 259
40, 2, 31.9, 95.00, 198, 135.6, 38, 5.00, 4.8040, 93, 178
33, 1, 35.0, 89.00, 200, 130.4, 42, 4.76, 4.9273, 101, 128
32, 2, 27.8, 89.00, 216, 146.2, 55, 4.00, 4.3041, 91, 96
35, 2, 25.9, 81.00, 174, 102.4, 31, 6.00, 5.3132, 82, 126
55, 1, 32.9, 102.00, 164, 106.2, 41, 4.00, 4.4308, 89, 288
49, 1, 26.0, 93.00, 183, 100.2, 64, 3.00, 4.5433, 88, 88
39, 2, 26.3, 115.00, 218, 158.2, 32, 7.00, 4.9345, 109, 292
60, 2, 22.3, 113.00, 186, 125.8, 46, 4.00, 4.2627, 94, 71
67, 2, 28.3, 93.00, 204, 132.2, 49, 4.00, 4.7362, 92, 197
41, 2, 32.0, 109.00, 251, 170.6, 49, 5.00, 5.0562, 103, 186
44, 1, 25.4, 95.00, 162, 92.6, 53, 3.00, 4.4067, 83, 25
48, 2, 23.3, 89.33, 212, 142.8, 46, 4.61, 4.7536, 98, 84
45, 1, 20.3, 74.33, 190, 126.2, 49, 3.88, 4.3041, 79, 96
47, 1, 30.4, 120.00, 199, 120.0, 46, 4.00, 5.1059, 87, 195
46, 1, 20.6, 73.00, 172, 107.0, 51, 3.00, 4.2485, 80, 53
36, 2, 32.3, 115.00, 286, 199.4, 39, 7.00, 5.4723, 112, 217
34, 1, 29.2, 73.00, 172, 108.2, 49, 4.00, 4.3041, 91, 172
53, 2, 33.1, 117.00, 183, 119.0, 48, 4.00, 4.3820, 106, 131
61, 1, 24.6, 101.00, 209, 106.8, 77, 3.00, 4.8363, 88, 214
37, 1, 20.2, 81.00, 162, 87.8, 63, 3.00, 4.0254, 88, 59
33, 2, 20.8, 84.00, 125, 70.2, 46, 3.00, 3.7842, 66, 70
68, 1, 32.8, 105.67, 205, 116.4, 40, 5.13, 5.4931, 117, 220
49, 2, 31.9, 94.00, 234, 155.8, 34, 7.00, 5.3982, 122, 268
48, 1, 23.9, 109.00, 232, 105.2, 37, 6.00, 6.1070, 96, 152
55, 2, 24.5, 84.00, 179, 105.8, 66, 3.00, 3.5835, 87, 47
43, 1, 22.1, 66.00, 134, 77.2, 45, 3.00, 4.0775, 80, 74
60, 2, 33.0, 97.00, 217, 125.6, 45, 5.00, 5.4467, 112, 295
31, 2, 19.0, 93.00, 137, 73.0, 47, 3.00, 4.4427, 78, 101
53, 2, 27.3, 82.00, 119, 55.0, 39, 3.00, 4.8283, 93, 151
67, 1, 22.8, 87.00, 166, 98.6, 52, 3.00, 4.3438, 92, 127
61, 2, 28.2, 106.00, 204, 132.0, 52, 4.00, 4.6052, 96, 237
62, 1, 28.9, 87.33, 206, 127.2, 33, 6.24, 5.4337, 99, 225
60, 1, 25.6, 87.00, 207, 125.8, 69, 3.00, 4.1109, 84, 81
42, 1, 24.9, 91.00, 204, 141.8, 38, 5.00, 4.7958, 89, 151
38, 2, 26.8, 105.00, 181, 119.2, 37, 5.00, 4.8203, 91, 107
62, 1, 22.4, 79.00, 222, 147.4, 59, 4.00, 4.3567, 76, 64
61, 2, 26.9, 111.00, 236, 172.4, 39, 6.00, 4.8122, 89, 138
61, 2, 23.1, 113.00, 186, 114.4, 47, 4.00, 4.8122, 105, 185
53, 1, 28.6, 88.00, 171, 98.8, 41, 4.00, 5.0499, 99, 265
28, 2, 24.7, 97.00, 175, 99.6, 32, 5.00, 5.3799, 87, 101
26, 2, 30.3, 89.00, 218, 152.2, 31, 7.00, 5.1591, 82, 137
30, 1, 21.3, 87.00, 134, 63.0, 63, 2.00, 3.6889, 66, 143
50, 1, 26.1, 109.00, 243, 160.6, 62, 4.00, 4.6250, 89, 141
48, 1, 20.2, 95.00, 187, 117.4, 53, 4.00, 4.4188, 85, 79
51, 1, 25.2, 103.00, 176, 112.2, 37, 5.00, 4.8978, 90, 292
47, 2, 22.5, 82.00, 131, 66.8, 41, 3.00, 4.7536, 89, 178
64, 2, 23.5, 97.00, 203, 129.0, 59, 3.00, 4.3175, 77, 91
51, 2, 25.9, 76.00, 240, 169.0, 39, 6.00, 5.0752, 96, 116
30, 1, 20.9, 104.00, 152, 83.8, 47, 3.00, 4.6634, 97, 86
56, 2, 28.7, 99.00, 208, 146.4, 39, 5.00, 4.7274, 97, 122
42, 1, 22.1, 85.00, 213, 138.6, 60, 4.00, 4.2767, 94, 72
62, 2, 26.7, 115.00, 183, 124.0, 35, 5.00, 4.7875, 100, 129
34, 1, 31.4, 87.00, 149, 93.8, 46, 3.00, 3.8286, 77, 142
60, 1, 22.2, 104.67, 221, 105.4, 60, 3.68, 5.6276, 93, 90
64, 1, 21.0, 92.33, 227, 146.8, 65, 3.49, 4.3307, 102, 158
39, 2, 21.2, 90.00, 182, 110.4, 60, 3.00, 4.0604, 98, 39
71, 2, 26.5, 105.00, 281, 173.6, 55, 5.00, 5.5683, 84, 196
48, 2, 29.2, 110.00, 218, 151.6, 39, 6.00, 4.9200, 98, 222
79, 2, 27.0, 103.00, 169, 110.8, 37, 5.00, 4.6634, 110, 277
40, 1, 30.7, 99.00, 177, 85.4, 50, 4.00, 5.3375, 85, 99
49, 2, 28.8, 92.00, 207, 140.0, 44, 5.00, 4.7449, 92, 196
51, 1, 30.6, 103.00, 198, 106.6, 57, 3.00, 5.1475, 100, 202
57, 1, 30.1, 117.00, 202, 139.6, 42, 5.00, 4.6250, 120, 155
59, 2, 24.7, 114.00, 152, 104.8, 29, 5.00, 4.5109, 88, 77
51, 1, 27.7, 99.00, 229, 145.6, 69, 3.00, 4.2767, 77, 191
74, 1, 29.8, 101.00, 171, 104.8, 50, 3.00, 4.3944, 86, 70
67, 1, 26.7, 105.00, 225, 135.4, 69, 3.00, 4.6347, 96, 73
49, 1, 19.8, 88.00, 188, 114.8, 57, 3.00, 4.3944, 93, 49
57, 1, 23.3, 88.00, 155, 63.6, 78, 2.00, 4.2047, 78, 65
56, 2, 35.1, 123.00, 164, 95.0, 38, 4.00, 5.0434, 117, 263
52, 2, 29.7, 109.00, 228, 162.8, 31, 8.00, 5.1417, 103, 248
69, 1, 29.3, 124.00, 223, 139.0, 54, 4.00, 5.0106, 102, 296
37, 1, 20.3, 83.00, 185, 124.6, 38, 5.00, 4.7185, 88, 214
24, 1, 22.5, 89.00, 141, 68.0, 52, 3.00, 4.6540, 84, 185
55, 2, 22.7, 93.00, 154, 94.2, 53, 3.00, 3.5264, 75, 78
36, 1, 22.8, 87.00, 178, 116.0, 41, 4.00, 4.6540, 82, 93
42, 2, 24.0, 107.00, 150, 85.0, 44, 3.00, 4.6540, 96, 252
21, 1, 24.2, 76.00, 147, 77.0, 53, 3.00, 4.4427, 79, 150
41, 1, 20.2, 62.00, 153, 89.0, 50, 3.00, 4.2485, 89, 77
57, 2, 29.4, 109.00, 160, 87.6, 31, 5.00, 5.3327, 92, 208
20, 2, 22.1, 87.00, 171, 99.6, 58, 3.00, 4.2047, 78, 77
67, 2, 23.6, 111.33, 189, 105.4, 70, 2.70, 4.2195, 93, 108
34, 1, 25.2, 77.00, 189, 120.6, 53, 4.00, 4.3438, 79, 160
41, 2, 24.9, 86.00, 192, 115.0, 61, 3.00, 4.3820, 94, 53
38, 2, 33.0, 78.00, 301, 215.0, 50, 6.02, 5.1930, 108, 220
51, 1, 23.5, 101.00, 195, 121.0, 51, 4.00, 4.7449, 94, 154
52, 2, 26.4, 91.33, 218, 152.0, 39, 5.59, 4.9053, 99, 259
67, 1, 29.8, 80.00, 172, 93.4, 63, 3.00, 4.3567, 82, 90
61, 1, 30.0, 108.00, 194, 100.0, 52, 3.73, 5.3471, 105, 246
67, 2, 25.0, 111.67, 146, 93.4, 33, 4.42, 4.5850, 103, 124
56, 1, 27.0, 105.00, 247, 160.6, 54, 5.00, 5.0876, 94, 67
64, 1, 20.0, 74.67, 189, 114.8, 62, 3.05, 4.1109, 91, 72
58, 2, 25.5, 112.00, 163, 110.6, 29, 6.00, 4.7622, 86, 257
55, 1, 28.2, 91.00, 250, 140.2, 67, 4.00, 5.3660, 103, 262
62, 2, 33.3, 114.00, 182, 114.0, 38, 5.00, 5.0106, 96, 275
57, 2, 25.6, 96.00, 200, 133.0, 52, 3.85, 4.3175, 105, 177
20, 2, 24.2, 88.00, 126, 72.2, 45, 3.00, 3.7842, 74, 71
53, 2, 22.1, 98.00, 165, 105.2, 47, 4.00, 4.1589, 81, 47
32, 2, 31.4, 89.00, 153, 84.2, 56, 3.00, 4.1589, 90, 187
41, 1, 23.1, 86.00, 148, 78.0, 58, 3.00, 4.0943, 60, 125
60, 1, 23.4, 76.67, 247, 148.0, 65, 3.80, 5.1358, 77, 78
26, 1, 18.8, 83.00, 191, 103.6, 69, 3.00, 4.5218, 69, 51
37, 1, 30.8, 112.00, 282, 197.2, 43, 7.00, 5.3423, 101, 258
45, 1, 32.0, 110.00, 224, 134.2, 45, 5.00, 5.4116, 93, 215
67, 1, 31.6, 116.00, 179, 90.4, 41, 4.00, 5.4723, 100, 303
34, 2, 35.5, 120.00, 233, 146.6, 34, 7.00, 5.5683, 101, 243
50, 1, 31.9, 78.33, 207, 149.2, 38, 5.45, 4.5951, 84, 91
71, 1, 29.5, 97.00, 227, 151.6, 45, 5.00, 5.0239, 108, 150
57, 2, 31.6, 117.00, 225, 107.6, 40, 6.00, 5.9584, 113, 310
49, 1, 20.3, 93.00, 184, 103.0, 61, 3.00, 4.6052, 93, 153
35, 1, 41.3, 81.00, 168, 102.8, 37, 5.00, 4.9488, 94, 346
41, 2, 21.2, 102.00, 184, 100.4, 64, 3.00, 4.5850, 79, 63
70, 2, 24.1, 82.33, 194, 149.2, 31, 6.26, 4.2341, 105, 89
52, 1, 23.0, 107.00, 179, 123.7, 42.5, 4.21, 4.1589, 93, 50
60, 1, 25.6, 78.00, 195, 95.4, 91, 2.00, 3.7612, 87, 39
62, 1, 22.5, 125.00, 215, 99.0, 98, 2.00, 4.4998, 95, 103
44, 2, 38.2, 123.00, 201, 126.6, 44, 5.00, 5.0239, 92, 308
28, 2, 19.2, 81.00, 155, 94.6, 51, 3.00, 3.8501, 87, 116
58, 2, 29.0, 85.00, 156, 109.2, 36, 4.00, 3.9890, 86, 145
39, 2, 24.0, 89.67, 190, 113.6, 52, 3.65, 4.8040, 101, 74
34, 2, 20.6, 98.00, 183, 92.0, 83, 2.00, 3.6889, 92, 45
65, 1, 26.3, 70.00, 244, 166.2, 51, 5.00, 4.8978, 98, 115
66, 2, 34.6, 115.00, 204, 139.4, 36, 6.00, 4.9628, 109, 264
51, 1, 23.4, 87.00, 220, 108.8, 93, 2.00, 4.5109, 82, 87
50, 2, 29.2, 119.00, 162, 85.2, 54, 3.00, 4.7362, 95, 202
59, 2, 27.2, 107.00, 158, 102.0, 39, 4.00, 4.4427, 93, 127
52, 1, 27.0, 78.33, 134, 73.0, 44, 3.05, 4.4427, 69, 182
69, 2, 24.5, 108.00, 243, 136.4, 40, 6.00, 5.8081, 100, 241
53, 1, 24.1, 105.00, 184, 113.4, 46, 4.00, 4.8122, 95, 66
47, 2, 25.3, 98.00, 173, 105.6, 44, 4.00, 4.7622, 108, 94
52, 1, 28.8, 113.00, 280, 174.0, 67, 4.00, 5.2730, 86, 283
39, 1, 20.9, 95.00, 150, 65.6, 68, 2.00, 4.4067, 95, 64
67, 2, 23.0, 70.00, 184, 128.0, 35, 5.00, 4.6540, 99, 102
59, 2, 24.1, 96.00, 170, 98.6, 54, 3.00, 4.4659, 85, 200
51, 2, 28.1, 106.00, 202, 122.2, 55, 4.00, 4.8203, 87, 265
23, 2, 18.0, 78.00, 171, 96.0, 48, 4.00, 4.9053, 92, 94
68, 1, 25.9, 93.00, 253, 181.2, 53, 5.00, 4.5433, 98, 230
44, 1, 21.5, 85.00, 157, 92.2, 55, 3.00, 3.8918, 84, 181
60, 2, 24.3, 103.00, 141, 86.6, 33, 4.00, 4.6728, 78, 156
52, 1, 24.5, 90.00, 198, 129.0, 29, 7.00, 5.2983, 86, 233
38, 1, 21.3, 72.00, 165, 60.2, 88, 2.00, 4.4308, 90, 60
61, 1, 25.8, 90.00, 280, 195.4, 55, 5.00, 4.9972, 90, 219
68, 2, 24.8, 101.00, 221, 151.4, 60, 4.00, 3.8712, 87, 80
28, 2, 31.5, 83.00, 228, 149.4, 38, 6.00, 5.3132, 83, 68
65, 2, 33.5, 102.00, 190, 126.2, 35, 5.00, 4.9698, 102, 332
69, 1, 28.1, 113.00, 234, 142.8, 52, 4.00, 5.2781, 77, 248
51, 1, 24.3, 85.33, 153, 71.6, 71, 2.15, 3.9512, 82, 84
29, 1, 35.0, 98.33, 204, 142.6, 50, 4.08, 4.0431, 91, 200
55, 2, 23.5, 93.00, 177, 126.8, 41, 4.00, 3.8286, 83, 55
34, 2, 30.0, 83.00, 185, 107.2, 53, 3.00, 4.8203, 92, 85
67, 1, 20.7, 83.00, 170, 99.8, 59, 3.00, 4.0254, 77, 89
49, 1, 25.6, 76.00, 161, 99.8, 51, 3.00, 3.9318, 78, 31
55, 2, 22.9, 81.00, 123, 67.2, 41, 3.00, 4.3041, 88, 129
59, 2, 25.1, 90.00, 163, 101.4, 46, 4.00, 4.3567, 91, 83
53, 1, 33.2, 82.67, 186, 106.8, 46, 4.04, 5.1120, 102, 275
48, 2, 24.1, 110.00, 209, 134.6, 58, 4.00, 4.4067, 100, 65
52, 1, 29.5, 104.33, 211, 132.8, 49, 4.31, 4.9836, 98, 198
69, 1, 29.6, 122.00, 231, 128.4, 56, 4.00, 5.4510, 86, 236
60, 2, 22.8, 110.00, 245, 189.8, 39, 6.00, 4.3944, 88, 253
46, 2, 22.7, 83.00, 183, 125.8, 32, 6.00, 4.8363, 75, 124
51, 2, 26.2, 101.00, 161, 99.6, 48, 3.00, 4.2047, 88, 44
67, 2, 23.5, 96.00, 207, 138.2, 42, 5.00, 4.8978, 111, 172
49, 1, 22.1, 85.00, 136, 63.4, 62, 2.19, 3.9703, 72, 114
46, 2, 26.5, 94.00, 247, 160.2, 59, 4.00, 4.9345, 111, 142
47, 1, 32.4, 105.00, 188, 125.0, 46, 4.09, 4.4427, 99, 109
75, 1, 30.1, 78.00, 222, 154.2, 44, 5.05, 4.7791, 97, 180
28, 1, 24.2, 93.00, 174, 106.4, 54, 3.00, 4.2195, 84, 144
65, 2, 31.3, 110.00, 213, 128.0, 47, 5.00, 5.2470, 91, 163
42, 1, 30.1, 91.00, 182, 114.8, 49, 4.00, 4.5109, 82, 147
51, 1, 24.5, 79.00, 212, 128.6, 65, 3.00, 4.5218, 91, 97
53, 2, 27.7, 95.00, 190, 101.8, 41, 5.00, 5.4638, 101, 220
54, 1, 23.2, 110.67, 238, 162.8, 48, 4.96, 4.9127, 108, 190
73, 1, 27.0, 102.00, 211, 121.0, 67, 3.00, 4.7449, 99, 109
54, 1, 26.8, 108.00, 176, 80.6, 67, 3.00, 4.9558, 106, 191
42, 1, 29.2, 93.00, 249, 174.2, 45, 6.00, 5.0039, 92, 122
75, 1, 31.2, 117.67, 229, 138.8, 29, 7.90, 5.7236, 106, 230
55, 2, 32.1, 112.67, 207, 92.4, 25, 8.28, 6.1048, 111, 242
68, 2, 25.7, 109.00, 233, 112.6, 35, 7.00, 6.0568, 105, 248
57, 1, 26.9, 98.00, 246, 165.2, 38, 7.00, 5.3660, 96, 249
48, 1, 31.4, 75.33, 242, 151.6, 38, 6.37, 5.5683, 103, 192
61, 2, 25.6, 85.00, 184, 116.2, 39, 5.00, 4.9698, 98, 131
69, 1, 37.0, 103.00, 207, 131.4, 55, 4.00, 4.6347, 90, 237
38, 1, 32.6, 77.00, 168, 100.6, 47, 4.00, 4.6250, 96, 78
45, 2, 21.2, 94.00, 169, 96.8, 55, 3.00, 4.4543, 102, 135
51, 2, 29.2, 107.00, 187, 139.0, 32, 6.00, 4.3820, 95, 244
71, 2, 24.0, 84.00, 138, 85.8, 39, 4.00, 4.1897, 90, 199
57, 1, 36.1, 117.00, 181, 108.2, 34, 5.00, 5.2679, 100, 270
56, 2, 25.8, 103.00, 177, 114.4, 34, 5.00, 4.9628, 99, 164
32, 2, 22.0, 88.00, 137, 78.6, 48, 3.00, 3.9512, 78, 72
50, 1, 21.9, 91.00, 190, 111.2, 67, 3.00, 4.0775, 77, 96
43, 1, 34.3, 84.00, 256, 172.6, 33, 8.00, 5.5294, 104, 306
54, 2, 25.2, 115.00, 181, 120.0, 39, 5.00, 4.7005, 92, 91
31, 1, 23.3, 85.00, 190, 130.8, 43, 4.00, 4.3944, 77, 214
56, 1, 25.7, 80.00, 244, 151.6, 59, 4.00, 5.1180, 95, 95
44, 1, 25.1, 133.00, 182, 113.0, 55, 3.00, 4.2485, 84, 216
57, 2, 31.9, 111.00, 173, 116.2, 41, 4.00, 4.3694, 87, 263

Test data:

# diabetes_test_100.txt
#
64, 2, 28.4, 111.00, 184, 127.0, 41, 4.00, 4.3820, 97, 178
43, 1, 28.1, 121.00, 192, 121.0, 60, 3.00, 4.0073, 93, 113
19, 1, 25.3, 83.00, 225, 156.6, 46, 5.00, 4.7185, 84, 200
71, 2, 26.1, 85.00, 220, 152.4, 47, 5.00, 4.6347, 91, 139
50, 2, 28.0, 104.00, 282, 196.8, 44, 6.00, 5.3279, 95, 139
59, 2, 23.6, 73.00, 180, 107.4, 51, 4.00, 4.6821, 84, 88
57, 1, 24.5, 93.00, 186, 96.6, 71, 3.00, 4.5218, 91, 148
49, 2, 21.0, 82.00, 119, 85.4, 23, 5.00, 3.9703, 74, 88
41, 2, 32.0, 126.00, 198, 104.2, 49, 4.00, 5.4116, 124, 243
25, 2, 22.6, 85.00, 130, 71.0, 48, 3.00, 4.0073, 81, 71
52, 2, 19.7, 81.00, 152, 53.4, 82, 2.00, 4.4188, 82, 77
34, 1, 21.2, 84.00, 254, 113.4, 52, 5.00, 6.0936, 92, 109
42, 2, 30.6, 101.00, 269, 172.2, 50, 5.00, 5.4553, 106, 272
28, 2, 25.5, 99.00, 162, 101.6, 46, 4.00, 4.2767, 94, 60
47, 2, 23.3, 90.00, 195, 125.8, 54, 4.00, 4.3307, 73, 54
32, 2, 31.0, 100.00, 177, 96.2, 45, 4.00, 5.1874, 77, 221
43, 1, 18.5, 87.00, 163, 93.6, 61, 2.67, 3.7377, 80, 90
59, 2, 26.9, 104.00, 194, 126.6, 43, 5.00, 4.8040, 106, 311
53, 1, 28.3, 101.00, 179, 107.0, 48, 4.00, 4.7875, 101, 281
60, 1, 25.7, 103.00, 158, 84.6, 64, 2.00, 3.8501, 97, 182
54, 2, 36.1, 115.00, 163, 98.4, 43, 4.00, 4.6821, 101, 321
35, 2, 24.1, 94.67, 155, 97.4, 32, 4.84, 4.8520, 94, 58
49, 2, 25.8, 89.00, 182, 118.6, 39, 5.00, 4.8040, 115, 262
58, 1, 22.8, 91.00, 196, 118.8, 48, 4.00, 4.9836, 115, 206
36, 2, 39.1, 90.00, 219, 135.8, 38, 6.00, 5.4205, 103, 233
46, 2, 42.2, 99.00, 211, 137.0, 44, 5.00, 5.0106, 99, 242
44, 2, 26.6, 99.00, 205, 109.0, 43, 5.00, 5.5797, 111, 123
46, 1, 29.9, 83.00, 171, 113.0, 38, 4.50, 4.5850, 98, 167
54, 1, 21.0, 78.00, 188, 107.4, 70, 3.00, 3.9703, 73, 63
63, 2, 25.5, 109.00, 226, 103.2, 46, 5.00, 5.9506, 87, 197
41, 2, 24.2, 90.00, 199, 123.6, 57, 4.00, 4.5218, 86, 71
28, 1, 25.4, 93.00, 141, 79.0, 49, 3.00, 4.1744, 91, 168
19, 1, 23.2, 75.00, 143, 70.4, 52, 3.00, 4.6347, 72, 140
61, 2, 26.1, 126.00, 215, 129.8, 57, 4.00, 4.9488, 96, 217
48, 1, 32.7, 93.00, 276, 198.6, 43, 6.42, 5.1475, 91, 121
54, 2, 27.3, 100.00, 200, 144.0, 33, 6.00, 4.7449, 76, 235
53, 2, 26.6, 93.00, 185, 122.4, 36, 5.00, 4.8903, 82, 245
48, 1, 22.8, 101.00, 110, 41.6, 56, 2.00, 4.1271, 97, 40
53, 1, 28.8, 111.67, 145, 87.2, 46, 3.15, 4.0775, 85, 52
29, 2, 18.1, 73.00, 158, 99.0, 41, 4.00, 4.4998, 78, 104
62, 1, 32.0, 88.00, 172, 69.0, 38, 4.00, 5.7838, 100, 132
50, 2, 23.7, 92.00, 166, 97.0, 52, 3.00, 4.4427, 93, 88
58, 2, 23.6, 96.00, 257, 171.0, 59, 4.00, 4.9053, 82, 69
55, 2, 24.6, 109.00, 143, 76.4, 51, 3.00, 4.3567, 88, 219
54, 1, 22.6, 90.00, 183, 104.2, 64, 3.00, 4.3041, 92, 72
36, 1, 27.8, 73.00, 153, 104.4, 42, 4.00, 3.4965, 73, 201
63, 2, 24.1, 111.00, 184, 112.2, 44, 4.00, 4.9345, 82, 110
47, 2, 26.5, 70.00, 181, 104.8, 63, 3.00, 4.1897, 70, 51
51, 2, 32.8, 112.00, 202, 100.6, 37, 5.00, 5.7746, 109, 277
42, 1, 19.9, 76.00, 146, 83.2, 55, 3.00, 3.6636, 79, 63
37, 2, 23.6, 94.00, 205, 138.8, 53, 4.00, 4.1897, 107, 118
28, 1, 22.1, 82.00, 168, 100.6, 54, 3.00, 4.2047, 86, 69
58, 1, 28.1, 111.00, 198, 80.6, 31, 6.00, 6.0684, 93, 273
32, 1, 26.5, 86.00, 184, 101.6, 53, 4.00, 4.9904, 78, 258
25, 2, 23.5, 88.00, 143, 80.8, 55, 3.00, 3.5835, 83, 43
63, 1, 26.0, 85.67, 155, 78.2, 46, 3.37, 5.0370, 97, 198
52, 1, 27.8, 85.00, 219, 136.0, 49, 4.00, 5.1358, 75, 242
# diabetes_test_100.txt
# 10 predictors, target y
# 100 items
#
65, 2, 28.5, 109.00, 201, 123.0, 46, 4.00, 5.0752, 96, 232
42, 1, 30.6, 121.00, 176, 92.8, 69, 3.00, 4.2627, 89, 175
53, 1, 22.2, 78.00, 164, 81.0, 70, 2.00, 4.1744, 101, 93
79, 2, 23.3, 88.00, 186, 128.4, 33, 6.00, 4.8122, 102, 168
43, 1, 35.4, 93.00, 185, 100.2, 44, 4.00, 5.3181, 101, 275
44, 1, 31.4, 115.00, 165, 97.6, 52, 3.00, 4.3438, 89, 293
62, 2, 37.8, 119.00, 113, 51.0, 31, 4.00, 5.0434, 84, 281
33, 1, 18.9, 70.00, 162, 91.8, 59, 3.00, 4.0254, 58, 72
56, 1, 35.0, 79.33, 195, 140.8, 42, 4.64, 4.1109, 96, 140
66, 1, 21.7, 126.00, 212, 127.8, 45, 4.71, 5.2781, 101, 189
34, 2, 25.3, 111.00, 230, 162.0, 39, 6.00, 4.9767, 90, 181
46, 2, 23.8, 97.00, 224, 139.2, 42, 5.00, 5.3660, 81, 209
50, 1, 31.8, 82.00, 136, 69.2, 55, 2.00, 4.0775, 85, 136
69, 1, 34.3, 113.00, 200, 123.8, 54, 4.00, 4.7095, 112, 261
34, 1, 26.3, 87.00, 197, 120.0, 63, 3.00, 4.2485, 96, 113
71, 2, 27.0, 93.33, 269, 190.2, 41, 6.56, 5.2417, 93, 131
47, 1, 27.2, 80.00, 208, 145.6, 38, 6.00, 4.8040, 92, 174
41, 1, 33.8, 123.33, 187, 127.0, 45, 4.16, 4.3175, 100, 257
34, 1, 33.0, 73.00, 178, 114.6, 51, 3.49, 4.1271, 92, 55
51, 1, 24.1, 87.00, 261, 175.6, 69, 4.00, 4.4067, 93, 84
43, 1, 21.3, 79.00, 141, 78.8, 53, 3.00, 3.8286, 90, 42
55, 1, 23.0, 94.67, 190, 137.6, 38, 5.00, 4.2767, 106, 146
59, 2, 27.9, 101.00, 218, 144.2, 38, 6.00, 5.1874, 95, 212
27, 2, 33.6, 110.00, 246, 156.6, 57, 4.00, 5.0876, 89, 233
51, 2, 22.7, 103.00, 217, 162.4, 30, 7.00, 4.8122, 80, 91
49, 2, 27.4, 89.00, 177, 113.0, 37, 5.00, 4.9053, 97, 111
27, 1, 22.6, 71.00, 116, 43.4, 56, 2.00, 4.4188, 79, 152
57, 2, 23.2, 107.33, 231, 159.4, 41, 5.63, 5.0304, 112, 120
39, 2, 26.9, 93.00, 136, 75.4, 48, 3.00, 4.1431, 99, 67
62, 2, 34.6, 120.00, 215, 129.2, 43, 5.00, 5.3660, 123, 310
37, 1, 23.3, 88.00, 223, 142.0, 65, 3.40, 4.3567, 82, 94
46, 1, 21.1, 80.00, 205, 144.4, 42, 5.00, 4.5326, 87, 183
68, 2, 23.5, 101.00, 162, 85.4, 59, 3.00, 4.4773, 91, 66
51, 1, 31.5, 93.00, 231, 144.0, 49, 4.70, 5.2523, 117, 173
41, 1, 20.8, 86.00, 223, 128.2, 83, 3.00, 4.0775, 89, 72
53, 1, 26.5, 97.00, 193, 122.4, 58, 3.00, 4.1431, 99, 49
45, 1, 24.2, 83.00, 177, 118.4, 45, 4.00, 4.2195, 82, 64
33, 1, 19.5, 80.00, 171, 85.4, 75, 2.00, 3.9703, 80, 48
60, 2, 28.2, 112.00, 185, 113.8, 42, 4.00, 4.9836, 93, 178
47, 2, 24.9, 75.00, 225, 166.0, 42, 5.00, 4.4427, 102, 104
60, 2, 24.9, 99.67, 162, 106.6, 43, 3.77, 4.1271, 95, 132
36, 1, 30.0, 95.00, 201, 125.2, 42, 4.79, 5.1299, 85, 220
36, 1, 19.6, 71.00, 250, 133.2, 97, 3.00, 4.5951, 92, 57