Machine_Learning_algorithm_recipes_in_scikit－learn

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详细说明：Machine_Learning_algorithm_recipes_in_scikit－learns.pdf/∠U1b Get r our Hands dirty vvitn Sci KI t-Learn NoW -Macnine Learning Mvlastery 16 print(metrics confusion-matrix(expected, predicted)) For more information see the API reference for Logistic Regression for details on configuring the algorithm parameters. Also see the Logistic Regression section of the user guide Naive Bayes Naive Bayes uses Bayes Theorem to model the conditional relationship of each attribute to the class variable This recipe shows the fitting of an Naive Bayes model to the iris dataset 1# Gaussian Naive bayes 2 from skLearn import datasets 3 from sklearn import metrics 4 from sklearn. naive bayes import gaussianNB 5# load the iris datasets dataset= datasets Load_iris 7# fit a naive bayes model to the data 8 model= gaussianNBO 9 modeL, fit(dataset, data, dataset target) 10 print(model) 11# make predictions 12 expected =dataset target 13 14 summarize the fit of the model 15print(metrics classification_report(expected, predicted)) 16 print(metrics. confusion_matrix(expected, predicted)) For more information see the API reference for the Gaussian Naive Bayes for details on configuring the algorithm parameters. Also see the Naive bayes section of the user guide K-Nearest Neighbor The k-Nearest Neighbor(kNN) method makes predictions by locating similar cases to a given data instance(using a similarity function)and returning the average or majority of the most similar data instances. The kNN algorithm can be used for classification or regression This recipe shows use of the kNN model to make predictions for the iris dataset 1# k-Nearest Neighbor 2 from sklearn import datasets 3 from sklearn import metrics 4 from sklearn neighbors import KNeighborsClassifier 5# Load iris the datasets 6 dataset datasets, load iris 7# fit a k-nearest neighbor model to the data 8 model= KNeighborsCLassifiero 10 print(model) 11# make predictions 12 expected dataset target http:/machinelearningmasterycom/aet-your-hands-dirt-with-scikit-learn-now/ /∠U1b Get r our Hands dirty vvitn Sci KI t-Learn NoW -Macnine Learning Mvlastery 14 #f summarize the fit of the modeL 15 print classification report( 16 print(metrics. confusion_matrix(expected, predicted)) For more information see the API reference for the k-Nearest Neighbor for details on configuring the algorithm parameters. Also see the k-Nearest Neighbor section of the user guide Classification and regression Trees Classification and Regression Trees(CART) are constructed from a dataset by making splits that best separate the data for the classes or predictions being made. The CART algorithm can be used for classification or regression This recipe shows use of the CART model to make predictions for the iris dataset 1#Decision Tree Classifier 2 from sklearn import datasets 3 from sklearn import metrics from sklearn tree import Decision T recLassifier 5# load the iris datasets 6 dataset= datasets, load_iriso 7# fit a CART model to the data 8 model= DecisionTreeClassifiero 10 print(model) 11# make predictions 12 expected= dataset target 13 predicted model predict(dataset data) 14 summarize the fit of the mode l 15print(metrics classification_report(expected, predicted) 16 print(metrics. confusion_matrix(expected, predicted)) For more information see the API reference for CART for details on configuring the algorithm parameters. Also see the Decision Tree section of the user guide Support Vector Machines Support Vector Machines(SVM) are a method that uses points in a transformed problem space that best separate classes into two groups Classification for multiple classes is supported by a one-VS-all method. SVM also supports regression by modeling the function with a minimum amount of allowable error This recipe shows use of the svm model to make predictions for the iris dataset 1# Support Vector Machine 2 from sklearn import datasets om sklearn import metrics 4 from sklearn svm import Svc 5# load the iris datasets 6 dataset datasets Load_iris httn'/m achinelearninam com/aet-vou r-hands-dirtvy_with- scikit-learn-now/ /∠U1 Get rour Manas uirty viN SCIKIT-Learn INow-Macnine Learning Mastery 7# fit a Svm model to the data 8 model= Svco 9 model fit(dataset, data, dataset. t 10print(model) 11# make predictions 2 expected datasettarget 13 predicted el, predict(dataset data 14 #f summarize the fit of the model classification_report( 16 print(metrics. confusion_matrix(expected, predicted)) For more information see the API reference for SVM for details on configuring the algorithm parameters. Also see the SVM section of the user guide Summary In this post you have seen 5 self-contained recipes demonstrating some of the most popular and powerful supervised classification problems Each example is less than 20 lines that you can copy and paste and start using scikit-learn, right now Stop reading and start practicing. Pick one recipe and run it, then start to play with the parameters and see what effect that has on the results Take The Next Step Are you looking to get started or make the most of the scikit Jump-start scikit-Learn learn library without getting bogged down with the mathematics pply Machine Learning with Scikit-Learn Now and theory of the algorithms? In this 35-page pdf guide you will dis cover 35 standalone scikit learn recipes that you can copy-paste into your project Jump-Start Scikit-Learn Recipes cover data handling, supervised learning algorithm, LEARNING regularization, ensemble methods and advanced topics like feature selection, cross validation and parameter tuning If you want to get up and running with scikit-learn fast, this recipe book is for you! About jason brownlee Editor and Chief at MachineLearning Mastery. com. Dr Brownlee is a husband, father professional programmer and a machine learning enthusiast. Learn more about him View all posts by Jason Brownlee-y httn'i/m achinelearningm asterv com/oet-vou r-hands_dirty -with- scikit-learn-noMw/ /∠U1 Get Y our Hands Dirty vvitn SclKIt-Learn Now -Macnine Learning Mastery k The best Machine Learning algorithm Prepare Data for Machine Learning in Python with Pandas> N。 comments yet. Leave a Reply Name(required) Email(will not be published)(required) Website SUBMIT COMMENT Search Resources you can use to learn faster Feeling overwhelmed? Download your guide to the best hand-picked machine learning books, course, tools and other resources httn'i/m achinelearningm asterv com/oet-vou r-hands_dirty -with- scikit-learn-noMw/ /∠U1 Get Y our Hands Dirty vvitn SclKIt-Learn Now -Macnine Learning Mastery Machine Leaming Resource Guide MACHINE LEARNING MASTER POPULAR A Tour of Machine Learning Algorithms NOVEMBER 25. 2013 How to Run Your first Class ifier in Weka FEBRUARY 17 2014 Tutorial To Implement k-Nearest Neighbors in Python From Scratch SEPTEMBER 12. 2014 Discover Feature Engineering How to Engineer Features and How to get Good at It SEPTEMBER 26. 2014 Best Machine Learning Resources for Getting Started NOVEMBER 27 2013 4 Self-Study Machine Learning Projects JANUARY 3. 2014 5 Mistakes Programmers Make when Starting in Machine Learning JANUARY 29.2014 Why Get Into Machine Learning? 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