机器学习-sklearn-learn

随即森林

from sklearn import neighbors, datasets, preprocessing

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
iris = datasets.load_iris()
X,y = iris.data[:,:2],iris.target
X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=33)
scaler = preprocessing.StandardScaler().fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
knn = neighbors.KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train,y_train)
y_pred = knn.predict(X_test)
sum(y_test == y_pred)/y_test.shape[0]
accuracy_score(y_test,y_pred)

决策树：

from sklearn import neighbors, datasets, preprocessing

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
iris = datasets.load_iris()
X,y = iris.data[:,:2],iris.target
X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=33)
scaler = preprocessing.StandardScaler().fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
dt = DecisionTreeClassifier()
dt.fit(X_train,y_train)
tree_result = dt.predict(X_test)
accuracy_score(tree_result,y_test)