概述
将多分类转换为二分类来进行相应的计算
相关代码：
1. LogisticRegression
from sklearn import datasets | |
from sklearn.model_selection import train_test_split | |
from sklearn.multiclass import OneVsRestClassifier | |
from sklearn.multiclass import OneVsOneClassifier | |
from sklearn.linear_model import LogisticRegression | |
from sklearn.metrics import accuracy_score | |
data_iris = datasets.load_iris() | |
x, y = data_iris.data, data_iris.target | |
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3,random_state = 0) | |
# 使用multiclass的OvO多分类策略,分类器使用LogisticRegression | |
model = OneVsOneClassifier(LogisticRegression(C=1.0, tol=1e-6)) | |
model.fit(x_train, y_train) | |
y_pred = model.predict(x_test) | |
print(accuracy_score(y_test, y_pred)) | |
============================================ | |
0.9555555555555556 | |
# 使用multiclass的OvR多分类策略,分类器使用LogisticRegression | |
model = OneVsRestClassifier(LogisticRegression(C=1.0, tol=1e-6)) | |
model.fit(x_train, y_train) | |
y_pred = model.predict(x_test) | |
print(accuracy_score(y_test, y_pred)) | |
============================================ | |
0.8888888888888888 |
2. SVM
SVC
from sklearn import datasets | |
from sklearn.model_selection import train_test_split | |
from sklearn.multiclass import OneVsRestClassifier | |
from sklearn.multiclass import OneVsOneClassifier | |
from sklearn import svm | |
from sklearn.metrics import accuracy_score | |
data_iris = datasets.load_iris() | |
x, y = data_iris.data, data_iris.target | |
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3,random_state = 0) | |
# 使用multiclass的OvO多分类策略,分类器使用SVM | |
model = OneVsOneClassifier(svm.SVC()) | |
model.fit(x_train, y_train) | |
y_pred = model.predict(x_test) | |
print(accuracy_score(y_test, y_pred)) | |
============================================ | |
0.9555555555555556 | |
# 使用multiclass的OvR多分类策略,分类器使用SVM | |
model = OneVsRestClassifier(svm.SVC()) | |
model.fit(x_train, y_train) | |
y_pred = model.predict(x_test) | |
print(accuracy_score(y_test, y_pred)) | |
============================================ | |
0.8888888888888888 |
LinearSVC
# 使用multiclass的OvR多分类策略,分类器使用SVM | |
model = OneVsRestClassifier(svm.LinearSVC()) | |
model.fit(x_train, y_train) | |
y_pred = model.predict(x_test) | |
print(accuracy_score(y_test, y_pred)) |
3. Decision Tree
from sklearn.tree import DecisionTreeClassifier | |
model = DecisionTreeClassifier() | |
model.fit(x_train, y_train) | |
y_pred = model.predict(x_test) | |
print(accuracy_score(y_test, y_pred)) |
4. Random Forest
from sklearn.ensemble import RandomForestClassifier | |
model = RandomForestClassifier(n_jobs=2) | |
model.fit(x_train, y_train) | |
y_pred = model.predict(x_test) | |
print(accuracy_score(y_test, y_pred)) |
5. AdaBoost
from sklearn.ensemble import AdaBoostClassifier | |
model = AdaBoostClassifier(DecisionTreeClassifier(), | |
algorithm="SAMME", | |
n_estimators=200, | |
learning_rate=0.5) | |
model.fit(x_train, y_train) | |
y_pred = model.predict(x_test) | |
print(accuracy_score(y_test, y_pred)) |
6. 朴素贝叶斯
MultinomialNB
from sklearn.naive_bayes import MultinomialNB, GaussianNB | |
model = MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True) | |
model.fit(x_train, y_train) | |
y_pred = model.predict(x_test) | |
print(accuracy_score(y_test, y_pred)) |
GaussianNB
model = GaussianNB() | |
model.fit(x_train, y_train) | |
y_pred = model.predict(x_test) | |
print(accuracy_score(y_test, y_pred)) |
BernoulliNB
model = BernoulliNB() | |
model.fit(x_train, y_train) | |
y_pred = model.predict(x_test) | |
print(accuracy_score(y_test, y_pred)) |
7. KNN
from sklearn.neighbors import KNeighborsClassifier | |
model = KNeighborsClassifier() | |
model.fit(x_train, y_train) | |
y_pred = model.predict(x_test) | |
print(accuracy_score(y_test, y_pred)) |
8. GradientBoosting
from sklearn.ensemble import GradientBoostingClassifier | |
model = GradientBoostingClassifier() | |
model.fit(x_train, y_train) | |
y_pred = model.predict(x_test) | |
print(accuracy_score(y_test, y_pred)) |
PYTHON 复制 全屏
9. XGBoost
from xgboost.sklearn import XGBClassifier | |
model = XGBClassifier() | |
model.fit(x_train, y_train) | |
y_pred = model.predict(x_test) | |
print(accuracy_score(y_test, y_pred)) |
最后
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