klearn python API

• LinearRegression

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

• LogisticRegression

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

• 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)
            
          

• SVM

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

• 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)
            
          

• 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)
            
          

• 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)
            
          

• RandomForest

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

• 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)
            
          

• PCA

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

References