简单上手scikit-learn 02 训练模型
Load Data
作者：physihy
# load iris data
from sklearn.datasets import load_iris
iris = load_iris()
# store feature matrix in "X"
X = iris.data
# store response vector in "X"
y = iris.target
print(X.shape)
print(y.shape)
(150, 4)
(150,)
KNN
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=1)
Name of the object does not matter
Can specify tuning parameters (aka “hyperparameters”) during this step
All parameters not specified are set to their defaults
print(knn)
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=1, p=2,
weights='uniform')
knn.fit(X,y)
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=1, p=2,
weights='uniform')
X_new = [[3, 5, 4, 2], [5, 4, 3, 2]]
knn.predict(X_new)
array([2, 1])
Using different value of K
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X,y)
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
knn.predict(X_new)
array([1, 1])
Using Logistic Regression
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression()
logreg.fit(X,y)
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
verbose=0, warm_start=False)
logreg.predict(X_new)
array([2, 0])
作者：physihy
