#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from sklearn import tree
from sklearn import datasets
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
dsWine = datasets.load_wine()
X = dsWine.data #dane, przypadki
y = dsWine.target #decyzja
dt = tree.DecisionTreeClassifier(max_depth=3)
# CV
#dt.fit(X,y) # to do CV
#result = cross_val_score(dt,X,y,cv=10)# to do CV
#print(sum(result)/len(result))# to do CV
# Koniec CV
# T&T
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3)
dt.fit(X_train,y_train)
result = dt.score(X_test, y_test)
print(result)
# Koniec T&T
Część (II) — przykład z zajęć (2)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from sklearn import tree
from sklearn import datasets
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
dsWine = datasets.load_wine()
X = dsWine.data #dane, przypadki
y = dsWine.target #decyzja
dt = tree.DecisionTreeClassifier(max_depth=3)
# CV
#dt.fit(X,y) # to do CV
#result = cross_val_score(dt,X,y,cv=10)# to do CV
#print(sum(result)/len(result))# to do CV
# Koniec CV
# T&T
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3)
dt.fit(X_train,y_train)
result = dt.score(X_test, y_test)
print(result)
# Koniec T&T
fData=[[1,1,1,1,1,1,1,1,1,1,1,1,1]]
pred=dt.predict(fData)
print(pred)
from sklearn.externals.six import StringIO
import pydot
dt_data = StringIO()
tree.export_graphviz(dt,out_file=dt_data)
graphDt = pydot.graph_from_dot_data(dt_data.getvalue())
graphDt[0].write_pdf(„drzewoWine.pdf”)