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Decision Trees with visualisations while buiding tree

Decision Trees with visualisation while building trees.

This example covers the use of

  1. Classification and

  2. Regression Tree

From *spkit* library with different verbosity mode while training and plotting resulting decision tree after training. We use two different datasets Iris and Breast Cancer for classification and Boston Housing price for Regression.

# Import libraries
import numpy as np
import matplotlib.pyplot as plt
import spkit
print('spkit version :', spkit.__version__)

# just to makesure same results
np.random.seed(11)

# import Classification and Regression Tree from spkit
from spkit.ml import ClassificationTree, RegressionTree

# import dataset and train-test split from sklearn or use your own dataset
from sklearn import datasets
from sklearn.model_selection import train_test_split

spkit version : 0.0.9.7

Classification Tree

# 1. Iris Dataset

# Loading and spliting for training and testing
data = datasets.load_iris()
X = data.data
y = data.target

feature_names = data.feature_names #Optional

Xt,Xs, yt, ys = train_test_split(X,y,test_size=0.3)

print('Shapes', X.shape,y.shape, Xt.shape, yt.shape, Xs.shape, ys.shape)

# Fitting a model (displaying the tree building) with different modes

# 1) verbose=0 (silence mode)

model = ClassificationTree()
model.fit(Xt,yt,verbose=0,feature_names=feature_names)


# 2) verbose=1 (progress bar)


model = ClassificationTree()
model.fit(Xt,yt,verbose=1,feature_names=feature_names)

# 3) verbose=2 (printing tree info)


model = ClassificationTree()
model.fit(Xt,yt,verbose=2,feature_names=feature_names)


# 4) verbose=3 (printing branches only)

model = ClassificationTree()
model.fit(Xt,yt,verbose=3,feature_names=feature_names)


# 5) verbose=4 (Plotting tree.. while building)


model = ClassificationTree()
model.fit(Xt,yt,verbose=4,feature_names=feature_names)


plt.figure(figsize=(10,6))
model.plotTree(show=True,scale=False)


plt.figure(figsize=(8,6))
model.plotTree(DiffBranchColor=False)


# Predicting

ytp = model.predict(Xt)
ysp = model.predict(Xs)


ytpr = model.predict_proba(Xt)[:,1]
yspr = model.predict_proba(Xs)[:,1]

print('Depth of trained Tree ', model.getTreeDepth())
print('Accuracy')
print('- Training : ',np.mean(ytp==yt))
print('- Testing  : ',np.mean(ysp==ys))
print('Logloss')
Trloss = -np.mean(yt*np.log(ytpr+1e-10)+(1-yt)*np.log(1-ytpr+1e-10))
Tsloss = -np.mean(ys*np.log(yspr+1e-10)+(1-ys)*np.log(1-yspr+1e-10))
print('- Training : ',Trloss)
print('- Testing  : ',Tsloss)


# Iris data with smaller tree


model = ClassificationTree(max_depth=3)
model.fit(Xt,yt,verbose=1,feature_names=feature_names)
plt.figure(figsize=(5,5))
model.plotTree(show=True,DiffBranchColor=True)
ytp = model.predict(Xt)
ysp = model.predict(Xs)

ytpr = model.predict_proba(Xt)[:,1]
yspr = model.predict_proba(Xs)[:,1]

print('Depth of trained Tree ', model.getTreeDepth())
print('Accuracy')
print('- Training : ',np.mean(ytp==yt))
print('- Testing  : ',np.mean(ysp==ys))
print('Logloss')
Trloss = -np.mean(yt*np.log(ytpr+1e-10)+(1-yt)*np.log(1-ytpr+1e-10))
Tsloss = -np.mean(ys*np.log(yspr+1e-10)+(1-ys)*np.log(1-yspr+1e-10))
print('- Training : ',Trloss)
print('- Testing  : ',Tsloss)
  • plot ml decision tree visualisation
  • Decision Tree
  • Decision Tree
  • Decision Tree
Shapes (150, 4) (150,) (105, 4) (105,) (45, 4) (45,)
Number of features:: 4
Number of samples :: 105
---------------------------------------
|Building the tree.....................
|subtrees::|25%|----->...............|\
|subtrees::|50%|---------->..........|-
|subtrees::|75%|--------------->.....|/
|subtrees::|100%|-------------------->||
|subtrees::|25%|----->...............|\
|subtrees::|50%|---------->..........|-
|subtrees::|75%|--------------->.....|/
|subtrees::|100%|-------------------->||
|subtrees::|25%|----->...............|\
|subtrees::|50%|---------->..........|-
|subtrees::|75%|--------------->.....|/
|subtrees::|100%|-------------------->||
|subtrees::|25%|----->...............|\
|subtrees::|50%|---------->..........|-
|subtrees::|75%|--------------->.....|/
|subtrees::|100%|-------------------->||
|subtrees::|25%|----->...............|\
|subtrees::|50%|---------->..........|-
|subtrees::|75%|--------------->.....|/
|subtrees::|100%|-------------------->||
|subtrees::|25%|----->...............|\
|subtrees::|50%|---------->..........|-
|subtrees::|75%|--------------->.....|/
|subtrees::|100%|-------------------->||
|subtrees::|25%|----->...............|\
|subtrees::|50%|---------->..........|-
|subtrees::|75%|--------------->.....|/
|subtrees::|100%|-------------------->||
|subtrees::|25%|----->...............|\
|subtrees::|50%|---------->..........|-
|subtrees::|75%|--------------->.....|/
|subtrees::|100%|-------------------->||
|subtrees::|25%|----->...............|\
|subtrees::|50%|---------->..........|-
|subtrees::|75%|--------------->.....|/
|subtrees::|100%|-------------------->||
|
|.........................tree is buit!
---------------------------------------
Number of features:: 4
Number of samples :: 105
---------------------------------------
|Building the tree.....................
|-Feature::1_sepal length (cm) Gain::0.01 thr::_Depth = 1
|-Feature::1_sepal length (cm) Gain::0.06 thr::_Depth = 1
|-Feature::1_sepal length (cm) Gain::0.09 thr::_Depth = 1
|-Feature::1_sepal length (cm) Gain::0.13 thr::_Depth = 1
|-Feature::1_sepal length (cm) Gain::0.21 thr::_Depth = 1
|-Feature::1_sepal length (cm) Gain::0.34 thr::_Depth = 1
|-Feature::1_sepal length (cm) Gain::0.4 thr::_Depth = 1
|-Feature::1_sepal length (cm) Gain::0.43 thr::_Depth = 1
|-Feature::1_sepal length (cm) Gain::0.57 thr::_Depth = 1
|-Feature::3_petal length (cm) Gain::0.64 thr::_Depth = 1
|-Feature::3_petal length (cm) Gain::0.8 thr::_Depth = 1
|-Feature::3_petal length (cm) Gain::0.93 thr::_Depth = 1
|
|->False branch (<<<)..
|->{Leaf Node:: value: 0 }_Depth =2

|
|->True branch (>>>)..
|--Feature::1_sepal length (cm) Gain::0.0 thr::_Depth = 2
|--Feature::1_sepal length (cm) Gain::0.0 thr::_Depth = 2
|--Feature::1_sepal length (cm) Gain::0.01 thr::_Depth = 2
|--Feature::1_sepal length (cm) Gain::0.02 thr::_Depth = 2
|--Feature::1_sepal length (cm) Gain::0.08 thr::_Depth = 2
|--Feature::1_sepal length (cm) Gain::0.09 thr::_Depth = 2
|--Feature::1_sepal length (cm) Gain::0.17 thr::_Depth = 2
|--Feature::1_sepal length (cm) Gain::0.2 thr::_Depth = 2
|--Feature::3_petal length (cm) Gain::0.21 thr::_Depth = 2
|--Feature::3_petal length (cm) Gain::0.26 thr::_Depth = 2
|--Feature::3_petal length (cm) Gain::0.35 thr::_Depth = 2
|--Feature::3_petal length (cm) Gain::0.4 thr::_Depth = 2
|--Feature::3_petal length (cm) Gain::0.45 thr::_Depth = 2
|--Feature::3_petal length (cm) Gain::0.46 thr::_Depth = 2
|--Feature::3_petal length (cm) Gain::0.56 thr::_Depth = 2
|--Feature::3_petal length (cm) Gain::0.69 thr::_Depth = 2
|--Feature::4_petal width (cm) Gain::0.81 thr::_Depth = 2
|
|-->False branch (<<<)..
|--Feature::1_sepal length (cm) Gain::0.0 thr::_Depth = 3
|--Feature::1_sepal length (cm) Gain::0.0 thr::_Depth = 3
|--Feature::1_sepal length (cm) Gain::0.0 thr::_Depth = 3
|--Feature::1_sepal length (cm) Gain::0.0 thr::_Depth = 3
|--Feature::1_sepal length (cm) Gain::0.01 thr::_Depth = 3
|--Feature::1_sepal length (cm) Gain::0.02 thr::_Depth = 3
|--Feature::1_sepal length (cm) Gain::0.03 thr::_Depth = 3
|--Feature::1_sepal length (cm) Gain::0.03 thr::_Depth = 3
|--Feature::1_sepal length (cm) Gain::0.04 thr::_Depth = 3
|--Feature::1_sepal length (cm) Gain::0.04 thr::_Depth = 3
|--Feature::2_sepal width (cm) Gain::0.04 thr::_Depth = 3
|--Feature::3_petal length (cm) Gain::0.05 thr::_Depth = 3
|--Feature::3_petal length (cm) Gain::0.06 thr::_Depth = 3
|--Feature::3_petal length (cm) Gain::0.07 thr::_Depth = 3
|--Feature::3_petal length (cm) Gain::0.11 thr::_Depth = 3
|--Feature::3_petal length (cm) Gain::0.13 thr::_Depth = 3
|--Feature::3_petal length (cm) Gain::0.18 thr::_Depth = 3
|
|-->False branch (<<<)..
|-->{Leaf Node:: value: 1 }_Depth =4

|
|-->True branch (>>>)..
|--->{Leaf Node:: value: 2 }_Depth =4

|
|-->True branch (>>>)..
|---Feature::1_sepal length (cm) Gain::0.0 thr::_Depth = 3
|---Feature::1_sepal length (cm) Gain::0.0 thr::_Depth = 3
|---Feature::1_sepal length (cm) Gain::0.01 thr::_Depth = 3
|---Feature::1_sepal length (cm) Gain::0.09 thr::_Depth = 3
|---Feature::3_petal length (cm) Gain::0.1 thr::_Depth = 3
|
|--->False branch (<<<)..
|---Feature::1_sepal length (cm) Gain::0.12 thr::_Depth = 4
|---Feature::1_sepal length (cm) Gain::0.31 thr::_Depth = 4
|---Feature::2_sepal width (cm) Gain::0.81 thr::_Depth = 4
|
|--->False branch (<<<)..
|--->{Leaf Node:: value: 2 }_Depth =5

|
|--->True branch (>>>)..
|---->{Leaf Node:: value: 1 }_Depth =5

|
|--->True branch (>>>)..
|---->{Leaf Node:: value: 2 }_Depth =4

|
|.........................tree is buit!
---------------------------------------
Number of features:: 4
Number of samples :: 105
---------------------------------------
|Building the tree.....................
None 1 |
True 2 | T
True 3 | TT
True 4 | TTT
False 4 | TTTF
True 5 | TTTFT
False 5 | TTTFF
False 3 | TTF
True 4 | TTFT
False 4 | TTFF
False 2 | TF
|
|.........................tree is buit!
---------------------------------------
Number of features:: 4
Number of samples :: 105
---------------------------------------
|Building the tree.....................
|
|.........................tree is buit!
---------------------------------------
Depth of trained Tree  4
Accuracy
- Training :  1.0
- Testing  :  0.9111111111111111
Logloss
- Training :  14.473392013068288
- Testing  :  15.350567286593632
Number of features:: 4
Number of samples :: 105
---------------------------------------
|Building the tree.....................
|subtrees::|25%|----->...............|\
|subtrees::|50%|---------->..........|-
|subtrees::|75%|--------------->.....|/
|subtrees::|100%|-------------------->||
|subtrees::|25%|----->...............|\
|subtrees::|50%|---------->..........|-
|subtrees::|75%|--------------->.....|/
|subtrees::|100%|-------------------->||
|subtrees::|25%|----->...............|\
|subtrees::|50%|---------->..........|-
|subtrees::|75%|--------------->.....|/
|subtrees::|100%|-------------------->||
|subtrees::|25%|----->...............|\
|subtrees::|50%|---------->..........|-
|subtrees::|75%|--------------->.....|/
|subtrees::|100%|-------------------->||
|subtrees::|25%|----->...............|\
|subtrees::|50%|---------->..........|-
|subtrees::|75%|--------------->.....|/
|subtrees::|100%|-------------------->||
|
|.........................tree is buit!
---------------------------------------
Depth of trained Tree  3
Accuracy
- Training :  0.9904761904761905
- Testing  :  0.8666666666666667
Logloss
- Training :  2.5937142862825335
- Testing  :  1.6081773385894438

Breast Cancer data

data = datasets.load_breast_cancer()
X = data.data
y = data.target

feature_names = data.feature_names #Optional

Xt,Xs, yt, ys = train_test_split(X,y,test_size=0.3)

print(X.shape,y.shape, Xt.shape, yt.shape, Xs.shape, ys.shape)


# Fitting model with displaying the details of tree in process (verbose=4)

# **While building tree, To first choose True branch and then False set randomBranch=False**


model = ClassificationTree()
model.fit(Xt,yt,verbose=4,feature_names=feature_names,randomBranch=False)
plt.show()


# **To randomly selevting True or False branch set randomBranch=True**

model = ClassificationTree()
model.fit(Xt,yt,verbose=4,feature_names=feature_names,randomBranch=True)
plt.show()


#  Resulting tree

plt.figure(figsize=(10,6))
model.plotTree(show=True,DiffBranchColor=True,scale=False)
plt.show()


#  Fitting model with displaying the progress only (verbose=1)


# %matplotlib inline
model = ClassificationTree()
model.fit(Xt,yt,verbose=1,feature_names=feature_names)

plt.figure(figsize=(6,6))
model.plotTree()
plt.show()

# Predicting

ytp = model.predict(Xt)
ysp = model.predict(Xs)

ytpr = model.predict_proba(Xt)[:,1]
yspr = model.predict_proba(Xs)[:,1]

print('Depth of trained Tree ', model.getTreeDepth())
print('Accuracy')
print('- Training : ',np.mean(ytp==yt))
print('- Testing  : ',np.mean(ysp==ys))
print('Logloss')
Trloss = -np.mean(yt*np.log(ytpr+1e-10)+(1-yt)*np.log(1-ytpr+1e-10))
Tsloss = -np.mean(ys*np.log(yspr+1e-10)+(1-ys)*np.log(1-yspr+1e-10))
print('- Training : ',Trloss)
print('- Testing  : ',Tsloss)
  • plot ml decision tree visualisation
  • plot ml decision tree visualisation
  • Decision Tree
  • Decision Tree
(569, 30) (569,) (398, 30) (398,) (171, 30) (171,)
Number of features:: 30
Number of samples :: 398
---------------------------------------
|Building the tree.....................
|
|.........................tree is buit!
---------------------------------------
Number of features:: 30
Number of samples :: 398
---------------------------------------
|Building the tree.....................
|
|.........................tree is buit!
---------------------------------------
Number of features:: 30
Number of samples :: 398
---------------------------------------
|Building the tree.....................
|subtrees::|3%|>....................|\
|subtrees::|6%|->...................|-
|subtrees::|10%|-->..................|/
|subtrees::|13%|-->..................||
|subtrees::|16%|--->.................|\
|subtrees::|20%|---->................|-
|subtrees::|23%|---->................|/
|subtrees::|26%|----->...............||
|subtrees::|30%|------>..............|\
|subtrees::|33%|------>..............|-
|subtrees::|36%|------->.............|/
|subtrees::|40%|-------->............||
|subtrees::|43%|-------->............|\
|subtrees::|46%|--------->...........|-
|subtrees::|50%|---------->..........|/
|subtrees::|53%|---------->..........||
|subtrees::|56%|----------->.........|\
|subtrees::|60%|------------>........|-
|subtrees::|63%|------------>........|/
|subtrees::|66%|------------->.......||
|subtrees::|70%|-------------->......|\
|subtrees::|73%|-------------->......|-
|subtrees::|76%|--------------->.....|/
|subtrees::|80%|---------------->....||
|subtrees::|83%|---------------->....|\
|subtrees::|86%|----------------->...|-
|subtrees::|90%|------------------>..|/
|subtrees::|93%|------------------>..||
|subtrees::|96%|------------------->.|\
|subtrees::|100%|-------------------->|-
|subtrees::|3%|>....................|\
|subtrees::|6%|->...................|-
|subtrees::|10%|-->..................|/
|subtrees::|13%|-->..................||
|subtrees::|16%|--->.................|\
|subtrees::|20%|---->................|-
|subtrees::|23%|---->................|/
|subtrees::|26%|----->...............||
|subtrees::|30%|------>..............|\
|subtrees::|33%|------>..............|-
|subtrees::|36%|------->.............|/
|subtrees::|40%|-------->............||
|subtrees::|43%|-------->............|\
|subtrees::|46%|--------->...........|-
|subtrees::|50%|---------->..........|/
|subtrees::|53%|---------->..........||
|subtrees::|56%|----------->.........|\
|subtrees::|60%|------------>........|-
|subtrees::|63%|------------>........|/
|subtrees::|66%|------------->.......||
|subtrees::|70%|-------------->......|\
|subtrees::|73%|-------------->......|-
|subtrees::|76%|--------------->.....|/
|subtrees::|80%|---------------->....||
|subtrees::|83%|---------------->....|\
|subtrees::|86%|----------------->...|-
|subtrees::|90%|------------------>..|/
|subtrees::|93%|------------------>..||
|subtrees::|96%|------------------->.|\
|subtrees::|100%|-------------------->|-
|subtrees::|3%|>....................|\
|subtrees::|6%|->...................|-
|subtrees::|10%|-->..................|/
|subtrees::|13%|-->..................||
|subtrees::|16%|--->.................|\
|subtrees::|20%|---->................|-
|subtrees::|23%|---->................|/
|subtrees::|26%|----->...............||
|subtrees::|30%|------>..............|\
|subtrees::|33%|------>..............|-
|subtrees::|36%|------->.............|/
|subtrees::|40%|-------->............||
|subtrees::|43%|-------->............|\
|subtrees::|46%|--------->...........|-
|subtrees::|50%|---------->..........|/
|subtrees::|53%|---------->..........||
|subtrees::|56%|----------->.........|\
|subtrees::|60%|------------>........|-
|subtrees::|63%|------------>........|/
|subtrees::|66%|------------->.......||
|subtrees::|70%|-------------->......|\
|subtrees::|73%|-------------->......|-
|subtrees::|76%|--------------->.....|/
|subtrees::|80%|---------------->....||
|subtrees::|83%|---------------->....|\
|subtrees::|86%|----------------->...|-
|subtrees::|90%|------------------>..|/
|subtrees::|93%|------------------>..||
|subtrees::|96%|------------------->.|\
|subtrees::|100%|-------------------->|-
|subtrees::|3%|>....................|\
|subtrees::|6%|->...................|-
|subtrees::|10%|-->..................|/
|subtrees::|13%|-->..................||
|subtrees::|16%|--->.................|\
|subtrees::|20%|---->................|-
|subtrees::|23%|---->................|/
|subtrees::|26%|----->...............||
|subtrees::|30%|------>..............|\
|subtrees::|33%|------>..............|-
|subtrees::|36%|------->.............|/
|subtrees::|40%|-------->............||
|subtrees::|43%|-------->............|\
|subtrees::|46%|--------->...........|-
|subtrees::|50%|---------->..........|/
|subtrees::|53%|---------->..........||
|subtrees::|56%|----------->.........|\
|subtrees::|60%|------------>........|-
|subtrees::|63%|------------>........|/
|subtrees::|66%|------------->.......||
|subtrees::|70%|-------------->......|\
|subtrees::|73%|-------------->......|-
|subtrees::|76%|--------------->.....|/
|subtrees::|80%|---------------->....||
|subtrees::|83%|---------------->....|\
|subtrees::|86%|----------------->...|-
|subtrees::|90%|------------------>..|/
|subtrees::|93%|------------------>..||
|subtrees::|96%|------------------->.|\
|subtrees::|100%|-------------------->|-
|subtrees::|3%|>....................|\
|subtrees::|6%|->...................|-
|subtrees::|10%|-->..................|/
|subtrees::|13%|-->..................||
|subtrees::|16%|--->.................|\
|subtrees::|20%|---->................|-
|subtrees::|23%|---->................|/
|subtrees::|26%|----->...............||
|subtrees::|30%|------>..............|\
|subtrees::|33%|------>..............|-
|subtrees::|36%|------->.............|/
|subtrees::|40%|-------->............||
|subtrees::|43%|-------->............|\
|subtrees::|46%|--------->...........|-
|subtrees::|50%|---------->..........|/
|subtrees::|53%|---------->..........||
|subtrees::|56%|----------->.........|\
|subtrees::|60%|------------>........|-
|subtrees::|63%|------------>........|/
|subtrees::|66%|------------->.......||
|subtrees::|70%|-------------->......|\
|subtrees::|73%|-------------->......|-
|subtrees::|76%|--------------->.....|/
|subtrees::|80%|---------------->....||
|subtrees::|83%|---------------->....|\
|subtrees::|86%|----------------->...|-
|subtrees::|90%|------------------>..|/
|subtrees::|93%|------------------>..||
|subtrees::|96%|------------------->.|\
|subtrees::|100%|-------------------->|-
|subtrees::|3%|>....................|\
|subtrees::|6%|->...................|-
|subtrees::|10%|-->..................|/
|subtrees::|13%|-->..................||
|subtrees::|16%|--->.................|\
|subtrees::|20%|---->................|-
|subtrees::|23%|---->................|/
|subtrees::|26%|----->...............||
|subtrees::|30%|------>..............|\
|subtrees::|33%|------>..............|-
|subtrees::|36%|------->.............|/
|subtrees::|40%|-------->............||
|subtrees::|43%|-------->............|\
|subtrees::|46%|--------->...........|-
|subtrees::|50%|---------->..........|/
|subtrees::|53%|---------->..........||
|subtrees::|56%|----------->.........|\
|subtrees::|60%|------------>........|-
|subtrees::|63%|------------>........|/
|subtrees::|66%|------------->.......||
|subtrees::|70%|-------------->......|\
|subtrees::|73%|-------------->......|-
|subtrees::|76%|--------------->.....|/
|subtrees::|80%|---------------->....||
|subtrees::|83%|---------------->....|\
|subtrees::|86%|----------------->...|-
|subtrees::|90%|------------------>..|/
|subtrees::|93%|------------------>..||
|subtrees::|96%|------------------->.|\
|subtrees::|100%|-------------------->|-
|subtrees::|3%|>....................|\
|subtrees::|6%|->...................|-
|subtrees::|10%|-->..................|/
|subtrees::|13%|-->..................||
|subtrees::|16%|--->.................|\
|subtrees::|20%|---->................|-
|subtrees::|23%|---->................|/
|subtrees::|26%|----->...............||
|subtrees::|30%|------>..............|\
|subtrees::|33%|------>..............|-
|subtrees::|36%|------->.............|/
|subtrees::|40%|-------->............||
|subtrees::|43%|-------->............|\
|subtrees::|46%|--------->...........|-
|subtrees::|50%|---------->..........|/
|subtrees::|53%|---------->..........||
|subtrees::|56%|----------->.........|\
|subtrees::|60%|------------>........|-
|subtrees::|63%|------------>........|/
|subtrees::|66%|------------->.......||
|subtrees::|70%|-------------->......|\
|subtrees::|73%|-------------->......|-
|subtrees::|76%|--------------->.....|/
|subtrees::|80%|---------------->....||
|subtrees::|83%|---------------->....|\
|subtrees::|86%|----------------->...|-
|subtrees::|90%|------------------>..|/
|subtrees::|93%|------------------>..||
|subtrees::|96%|------------------->.|\
|subtrees::|100%|-------------------->|-
|subtrees::|3%|>....................|\
|subtrees::|6%|->...................|-
|subtrees::|10%|-->..................|/
|subtrees::|13%|-->..................||
|subtrees::|16%|--->.................|\
|subtrees::|20%|---->................|-
|subtrees::|23%|---->................|/
|subtrees::|26%|----->...............||
|subtrees::|30%|------>..............|\
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|subtrees::|83%|---------------->....|\
|subtrees::|86%|----------------->...|-
|subtrees::|90%|------------------>..|/
|subtrees::|93%|------------------>..||
|subtrees::|96%|------------------->.|\
|subtrees::|100%|-------------------->|-
|subtrees::|3%|>....................|\
|subtrees::|6%|->...................|-
|subtrees::|10%|-->..................|/
|subtrees::|13%|-->..................||
|subtrees::|16%|--->.................|\
|subtrees::|20%|---->................|-
|subtrees::|23%|---->................|/
|subtrees::|26%|----->...............||
|subtrees::|30%|------>..............|\
|subtrees::|33%|------>..............|-
|subtrees::|36%|------->.............|/
|subtrees::|40%|-------->............||
|subtrees::|43%|-------->............|\
|subtrees::|46%|--------->...........|-
|subtrees::|50%|---------->..........|/
|subtrees::|53%|---------->..........||
|subtrees::|56%|----------->.........|\
|subtrees::|60%|------------>........|-
|subtrees::|63%|------------>........|/
|subtrees::|66%|------------->.......||
|subtrees::|70%|-------------->......|\
|subtrees::|73%|-------------->......|-
|subtrees::|76%|--------------->.....|/
|subtrees::|80%|---------------->....||
|subtrees::|83%|---------------->....|\
|subtrees::|86%|----------------->...|-
|subtrees::|90%|------------------>..|/
|subtrees::|93%|------------------>..||
|subtrees::|96%|------------------->.|\
|subtrees::|100%|-------------------->|-
|
|.........................tree is buit!
---------------------------------------
Depth of trained Tree  6
Accuracy
- Training :  1.0
- Testing  :  0.9298245614035088
Logloss
- Training :  -1.000000082690371e-10
- Testing  :  1.6158491879730155

Total running time of the script: (0 minutes 5.993 seconds)

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