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# 204.3.8 Practice : Validating the Tree

##### Finding the tree accuracy.

Link to the previous post : https://statinfer.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)
```
```[[6370   38]
[ 648 4749]]
```
```total = sum(sum(cm))
#####from confusion matrix calculate accuracy
accuracy = (cm[0,0]+cm[1,1])/total
accuracy
```
`0.94188903007200342`
• 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)
```
```0.94188903007200342

The next post is about the problem of overfitting the decision tree.
Link to the next post : https://statinfer.com/204-3-9-the-problem-of-overfitting-the-decision-tree/```
21st June 2017