12 練習：決策樹準確性

這裡優達的執行環境有個坑，就是他時而準確時而錯誤，所以測試的時候就一會兒是對的，一會兒是錯的，同樣的一個程式碼，感覺變數會混淆似的

import sys
from class_vis import prettyPicture
from prep_terrain_data import makeTerrainData

import matplotlib.pyplot as plt
import numpy as np
import pylab as pl

features_train, labels_train, features_test, labels_test = makeTerrainData()



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



### your code goes here--now create 2 decision tree classifiers,
### one with min_samples_split=2 and one with min_samples_split=50
### compute the accuracies on the testing data and store
### the accuracy numbers to acc_min_samples_split_2 and
### acc_min_samples_split_50, respectively

from sklearn import
 
 tree
from sklearn.metrics import accuracy_score
clf50 = tree.DecisionTreeClassifier(min_samples_split=50)
clf50.fit(features_train,labels_train)
result50 = clf50.predict(features_test)
acc_min_samples_split_50 = accuracy_score(labels_test,result50)

clf2 = tree.DecisionTreeClassifier(min_samples_split=2)
clf2.fit(features_train,labels_train)
result2 = clf2.predict(features_test)
acc_min_samples_split_2 = accuracy_score(labels_test,result2)


def
 
 submitAccuracies():
  return {"acc_min_samples_split_2":round(acc_min_samples_split_2,3),
          "acc_min_samples_split_50":round(acc_min_samples_split_50,3)}