Link to the previous post : https://course.dvanalyticsmds.com/204-3-7-building-a-decision-tree-in-python/
In the last post we built a decision tree and after plotting we explored the major characteristics of the tree.
In this post we will practice how to validate the tree.
Tree Validation
- Find the accuracy of the classification for the tree model
#Tree Validation
predict1 = clf.predict(X)
from sklearn.metrics import confusion_matrix ###for using confusion matrix###
cm = confusion_matrix(y, predict1)
print (cm)
total = sum(sum(cm))
#####from confusion matrix calculate accuracy
accuracy = (cm[0,0]+cm[1,1])/total
accuracy
- We can also use the .score() function to predict the accuracy in python from sklearn library.
- However, confusion matrix allows us to see the wrong classifications too that gives an intutive understanding.
clf.score(X,y)
