Machine Learning分類法

Machine Learning分類法有兩篇寫的淺顯昜懂，就不重覆造輪子了，直接看

https://ithelp.ithome.com.tw/articles/10187191

1、Decision Tree

from sklearn.datasets import load_iris

from sklearn import tree

from sklearn.cross_validation import train_test_split

from  sklearn import metrics

# 讀入鳶尾花資料

iris = load_iris()

iris_X = iris.data

iris_y = iris.target

# 切分訓練與測試資料

train_X, test_X, train_y, test_y = train_test_split(iris_X, iris_y, test_size = 0.3)

# 建立分類器

clf = tree.DecisionTreeClassifier()

iris_clf = clf.fit(train_X, train_y)

# 預測

test_y_predicted = iris_clf.predict(test_X)

print(test_y_predicted)

# 標準答案

print(test_y)

# 績效

accuracy = metrics.accuracy_score(test_y, test_y_predicted)

print(accuracy)

2、knn

from sklearn.datasets import load_iris

from sklearn import neighbors

from sklearn.cross_validation import train_test_split

from sklearn import metrics

# 讀入鳶尾花資料

iris = load_iris()

iris_X = iris.data

iris_y = iris.target

# 切分訓練與測試資料

train_X, test_X, train_y, test_y = train_test_split(iris_X, iris_y, test_size = 0.3)

# 建立分類器

clf = neighbors.KNeighborsClassifier()

iris_clf = clf.fit(train_X, train_y)

# 預測

test_y_predicted = iris_clf.predict(test_X)

print(test_y_predicted)

# 標準答案

print(test_y)

2、knn與svm的比較

https://www.jianshu.com/p/d8664bf9adf9

這樣就很清楚分類的用法啦!