sklearn python API

1、LinearRegression

from sklearn.linear_model import LinearRegression # 線性回歸 # module = LinearRegression() module.fit(x, y) module.score(x, y) module.predict(test)

2、LogisticRegression

from sklearn.linear_model import LogisticRegression # 邏輯回歸 # module = LogisticRegression() module.fit(x, y) module.score(x, y) module.predict(test)

3、KNN

from sklearn.neighbors import KNeighborsClassifier #K近鄰# from sklearn.neighbors import KNeighborsRegressor module = KNeighborsClassifier(n_neighbors=6) module.fit(x, y) predicted = module.predict(test) predicted = module.predict_proba(test)

4、SVM

from sklearn import svm #支持向量機# module = svm.SVC() module.fit(x, y) module.score(x, y) module.predict(test) module.predict_proba(test)

5、naive_bayes

from sklearn.naive_bayes import GaussianNB #樸素貝葉斯分類器# module = GaussianNB() module.fit(x, y) predicted = module.predict(test)

6、DecisionTree

from sklearn import tree #決策樹分類器# module = tree.DecisionTreeClassifier(criterion='gini') module.fit(x, y) module.score(x, y) module.predict(test)

7、K-Means

from sklearn.cluster import KMeans #kmeans聚類# module = KMeans(n_clusters=3, random_state=0) module.fit(x, y) module.predict(test)

8、RandomForest

from sklearn.ensemble import RandomForestClassifier #隨機森林# from sklearn.ensemble import RandomForestRegressor module = RandomForestClassifier() module.fit(x, y) module.predict(test)

9、GBDT

from sklearn.ensemble import GradientBoostingClassifier #Gradient Boosting 和 AdaBoost算法# from sklearn.ensemble import GradientBoostingRegressor module = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=1, random_state=0) module.fit(x, y) module.predict(test)

10、PCA

from sklearn.decomposition import PCA #PCA特征降維# train_reduced = PCA.fit_transform(train) test_reduced = PCA.transform(test)

總結

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