Python机器学习算法速查

2019-11-08 01:40:30

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常见的机器学习算法

以下是最常用的机器学习算法，大部分数据问题都可以通过它们解决：

线性回归 (Linear Regression)

逻辑回归 (Logistic Regression)

决策树 (Decision Tree)

支持向量机（SVM）

朴素贝叶斯 (Naive Bayes)

K邻近算法（KNN）

K-均值算法（K-means）

随机森林 (Random Forest)

降低维度算法（Dimensionality Reduction Algorithms）

Gradient Boost和Adaboost算法

1.线性回归 (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 arraysx_train=input_variables_values_training_datasetsy_train=target_variables_values_training_datasetsx_test=input_variables_values_test_datasets# Create linear regression objectlinear = linear_model.LinearRegression()# Train the model using the training sets and check scorelinear.fit(x_train, y_train)linear.score(x_train, y_train)#Equation coefficient and InterceptPRint('Coefficient: /n', linear.coef_)print('Intercept: /n', linear.intercept_)#Predict Outputpredicted= linear.predict(x_test)

2.逻辑回归 (Logistic Regression)

#Import Libraryfrom 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 objectmodel = LogisticRegression()# Train the model using the training sets and check scoremodel.fit(X, y)model.score(X, y)#Equation coefficient and Interceptprint('Coefficient: /n', model.coef_)print('Intercept: /n', model.intercept_)#Predict Outputpredicted= model.predict(x_test)

3.决策树 (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 scoremodel.fit(X, y)model.score(X, y)#Predict Outputpredicted= model.predict(x_test)

4.支持向量机（SVM）

#Import Libraryfrom 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 scoremodel.fit(X, y)model.score(X, y)#Predict Outputpredicted= model.predict(x_test)

5.朴素贝叶斯 (Naive Bayes)

#Import Libraryfrom 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 scoremodel.fit(X, y)#Predict Outputpredicted= model.predict(x_test)

6.K邻近算法（KNN）

#Import Libraryfrom 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 scoremodel.fit(X, y)#Predict Outputpredicted= model.predict(x_test)

7.K-均值算法（K-means )

#Import Libraryfrom 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 model = KMeans(n_clusters=3, random_state=0)# Train the model using the training sets and check scoremodel.fit(X)#Predict Outputpredicted= model.predict(x_test)

8.随机森林 (Random Forest)

#random forest#import libraryfrom 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 objectmodel=RandomForestClassifier()#train the model using the training sets and chek scoremodel.fit(x,y)#predict outputpredict=model.presort(x_test)

9.降低维度算法（Dimensionality Reduction Algorithms）

#Import Libraryfrom 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 PCAtrain_reduced = pca.fit_transform(train)#Reduced the dimension of test datasettest_reduced = pca.transform(test)

10.Gradient Boost和Adaboost算法

#Import Libraryfrom 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 objectmodel= GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=0)# Train the model using the training sets and check scoremodel.fit(X, y)#Predict Outputpredicted= model.predict(x_test)

以下实例中predict数据时为了验证其拟合度，采用的是训练集数据作为参数，实际中应该采用的是测试集，不要被误导了！！！

这里写图片描述