常用cheat sheet 整理(正则、git、jquery、机器学习等等)

线性回归

#Import Library
#Import other necessary libraries like pandas, numpy...
from sklearn import linear_model
#Load Train and Test datasets
#Identify feature and response variable(s) and values must be numeric and numpy arrays
x_train=input_variables_values_training_datasets
y_train=target_variables_values_training_datasets
x_test=input_variables_values_test_datasets
# Create linear regression object
linear = linear_model.LinearRegression()
# Train the model using the training sets and check score
linear.fit(x_train, y_train)
linear.score(x_train, y_train) 
#Equation coefficient and Intercept
print('Coefficient: n', linear.coef_)
print('Intercept: n', linear.intercept_)
#Predict Output
predicted= linear.predict(x_test)





逻辑回归
#Import Library
from sklearn.linear_model import LogisticRegression
#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
# Create logistic regression object
model = LogisticRegression()# Train the model using the training sets and check score
model.fit(X, y)
model.score(X, y)#Equation coefficient and Intercept
print('Coefficient: n', model.coef_)
print('Intercept: n', model.intercept_)#Predict Output
predicted= model.predict(x_test)




决策树
#Import Library
#Import other necessary libraries like pandas, numpy...
from sklearn import tree#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
# Create tree object 
model = tree.DecisionTreeClassifier(criterion='gini') # for classification, here you can change the algorithm as gini or entropy (information gain) by default it is gini  # model = tree.DecisionTreeRegressor() for regression
# Train the model using the training sets and check score
model.fit(X, y)
model.score(X, y)#Predict Output
predicted= model.predict(x_test)




支持向量机
#Import Library
from sklearn import svm#Assumed you have, X (predic
tor) and Y (target) for training data set and x_test(predictor) of test_dataset
# Create SVM classification object 
model = svm.svc() # there is various option associated with it, this is simple for classification. You can refer link, for mo# re detail.
# Train the model using the training sets and check score
model.fit(X, y)
model.score(X, y)#Predict Output
predicted= model.predict(x_test)






朴素贝叶斯
#Import Library
from sklearn.naive_bayes import GaussianNB#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
# Create SVM classification object model = GaussianNB() # there is other distribution for multinomial classes like Bernoulli Naive Bayes, Refer link
# Train the model using the training sets and check score
model.fit(X, y)#Predict Output
predicted= model.predict(x_test)




KNN（K – 最近邻算法）
#Import Library
from sklearn.neighbors import KNeighborsClassifier#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
# Create KNeighbors classifier object model 
KNeighborsClassifier(n_neighbors=6) # default value for n_neighbors is 5# Train the model using the training sets and check score
model.fit(X, y)#Predict Output
predicted= model.predict(x_test)




K 均值算法

#Import Library
from sklearn.cluster import KMeans#Assumed you have, X (attributes) for training data set and x_test(attributes) of test_dataset
# Create KNeighbors classifier object model 
k_means = KMeans(n_clusters=3, random_state=0)# Train the model using the training sets and check score
model.fit(X)#Predict Output
predicted= model.predict(x_test)

随机森林
#Import Library
from sklearn.ensemble import RandomForestClassifier#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
# Create Random Forest object
model= RandomForestClassifier()# Train the model using the training sets and check score
model.fit(X, y)#Predict Output
predicted= model.predict(x_test)




降维算法
#Import Library
from sklearn import decomposition#Assumed you have training and test data set as train and test
# Create PCA obeject pca= decomposition.PCA(n_components=k) #default value of k =min(n_sample, n_features)
# For Factor analysis
#fa= decomposition.FactorAnalysis()
# Reduced the dimension of training dataset using PCA
train_reduced = pca.fit_transform(train)#Reduced the dimension of test dataset
test_reduced = pca.transform(test)#For more detail on this, please refer  this link.







Gradient Boosting 和 AdaBoost 算法




#Import Library
from sklearn.ensemble import GradientBoostingClassifier#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
# Create Gradient Boosting Classifier object
model= GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=0)# Train the model using the training sets and check score
model.fit(X, y)#Predict Output
predicted= model.predict(x_test)


链接：http://blog.jobbole.com/92021/