Decision Tree

Decision Tree:very popular, oldest, most useful
->trick, non-linear decision making

wind surf
linearly separable?

Decision Trees:
two outcomes Yes or No? to classify official data.

X < 3, Y < 2 sk learning: decision tree http://scikit-learn.org/stable/modules/tree.html classification

>>> from sklearn import tree
>>> X = [[0, 0], [1, 1]]
>>> Y = [0, 1]
>>> clf = tree.DecisionTreeClassifier()
>>> clf = clf.fit(X, Y)
>>> clf.predict([[2., 2.]])
array([1])
>>> clf.predict_proba([[2., 2.]])
array([[ 0.,  1.]])
>>> from sklearn.datasets import load_iris
>>> from sklearn import tree
>>> iris = load_iris()
>>> clf = tree.DecisionTreeClassifier()
>>> clf = clf.fit(iris.data, iris.target)
>>> with open(“iris.dot”, ‘w’) as f:
…     f = tree.export_graphviz(clf, out_file=f)
>>> import os
>>> os.unlink(‘iris.dot’)
>>> import pydotplus 
>>> dot_data = tree.export_graphviz(clf, out_file=None) 
>>> graph = pydotplus.graph_from_dot_data(dot_data) 
>>> graph.write_pdf(“iris.pdf”)
>>> from IPython.display import Image  
>>> dot_data = tree.export_graphviz(clf, out_file=None, 
                         feature_names=iris.feature_names,  
                         class_names=iris.target_names,  
                         filled=True, rounded=True,  
                         special_characters=True)  
>>> graph = pydotplus.graph_from_dot_data(dot_data)  
>>> Image(graph.create_png())

DT decision boundary

import sys
from class_vis import prettyPicture
from prep_terrain_data import makeTerrainData

import numpy as np 
import pylab as pl

features_train, labels_train, features_test, labels_test = makeTerrainData()

acc =

def submitAccuracies():
	return {"acc":round(acc,3)}