import sys
from class_vis import prettyPicture
from prep_terrain_data import makeTerrainData

import numpy as np
import pylab as pl
from sklearn import tree
from sklearn.metrics import accuracy_score

features_train, labels_train, features_test, labels_test = makeTerrainData()



#################################################################################


########################## DECISION TREE #################################

clf=tree.DecisionTreeClassifier()
clf.fit(features_train,labels_train)
pred=clf.predict(features_test)

#### your code goes here



acc = accuracy_score(pred,labels_test)### you fill this in!
### be sure to compute the accuracy on the test set


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


7ff4f8766305d1982a9b1dfbfcb0a5dbed78162b
<mask token>

<mask token>
clf.fit(features_train, labels_train)
<mask token>


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

<mask token>
features_train, labels_train, features_test, labels_test = makeTerrainData()
clf = tree.DecisionTreeClassifier()
clf.fit(features_train, labels_train)
pred = clf.predict(features_test)
acc = accuracy_score(pred, labels_test)


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

import sys
from class_vis import prettyPicture
from prep_terrain_data import makeTerrainData
import numpy as np
import pylab as pl
from sklearn import tree
from sklearn.metrics import accuracy_score
features_train, labels_train, features_test, labels_test = makeTerrainData()
clf = tree.DecisionTreeClassifier()
clf.fit(features_train, labels_train)
pred = clf.predict(features_test)
acc = accuracy_score(pred, labels_test)


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

import sys
from class_vis import prettyPicture
from prep_terrain_data import makeTerrainData

import numpy as np
import pylab as pl
from sklearn import tree
from sklearn.metrics import accuracy_score

features_train, labels_train, features_test, labels_test = makeTerrainData()



#################################################################################


########################## DECISION TREE #################################

clf=tree.DecisionTreeClassifier()
clf.fit(features_train,labels_train)
pred=clf.predict(features_test)

#### your code goes here



acc = accuracy_score(pred,labels_test)### you fill this in!
### be sure to compute the accuracy on the test set


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

