用pyplot打印决策边界答案

【问题标题】：printing decision boundary with pyplot用pyplot打印决策边界
【发布时间】：2016-05-12 01:15:46
【问题描述】：

使用 pandas 和 sklearn 创建一个决策树来学习数据，我对树的修剪方法是重试不同的最大深度。我相信我一切正常，但我似乎无法通过 pyplot 输出它。有人可以帮我解决这个问题吗

import numpy as np
import pandas as pd
from sklearn import tree
from sklearn import cross_validation
from sklearn.cross_validation import KFold
import matplotlib.pyplot as plt

features = ['birad','age','Shape','margin','density','severity']

df = pd.read_csv('mammographic_masses.data',header=None,names=features)


df= df[df.birad != '?']
df= df[df.age != '?']
df= df[df.Shape != '?']
df= df[df.margin != '?']
df= df[df.density != '?']
#df= df[df.severity != '?']

x = df[features[:-1]]
y = df['severity']

x_train,x_test,y_train,y_test = cross_validation.train_test_split(x,y,test_size=0.4,random_state=0)

depth = []
best_depth = 3
best_score = 0
best_clf = []
for i in range(1,20):
    clf = tree.DecisionTreeClassifier(max_depth=i)
    clf = clf.fit(x_train,y_train)
    scores = cross_validation.cross_val_score(clf,x_train,y_train,cv=10)
    ascore = clf.score(x_test,y_test)
    depth.append((i,clf.score(x_test,y_test)))
    if ascore > best_score:
            best_score,best_depth = ascore,i
            best_clf.append(clf)
print best_depth,' ',best_score

【问题讨论】：

标签： python machine-learning decision-tree cross-validation

【解决方案1】：

只是一个猜测，因为你没有显示你试图在哪里绘制数据：你打电话了吗

plt.show()

在生成你的地块之后？在您执行此操作之前，它们实际上不会显示。

【讨论】：