机器学习算法速查

Linear Regression

#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)

Logistic Regression

#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)

Decision Tree

#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)

SVM

#Import Library
from sklearn import svm
#Assumed you have, X (predictor) 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)

Naive Bayes

#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

#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-means

#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)

Random Forest

#random forest
#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 chek score
model.fit(x,y)
#predict output
predict=model.presort(x_test)

Dimensionality Reduction Algorithms

#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)

Gradient Boost & Adaboost

#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)