spark machine learning example python

; "src=" Https://s5.51cto.com/oss/201710/26/fd22bb7084340218907c9863ffe8807a.png "style=" float: none; "title=" 1-1.png "alt=" Fd22bb7084340218907c9863ffe8807a.png "/>650) this.width=650; "src=" Https://s5.51cto.com/oss/201710/26/ce2a6dd0f4cc5e3f3198f223c8d23b6e.png "style=" float: none; "title=" 1-2.png "alt=" Ce2a6dd0f4cc5e3f3198f223c8d23b6e.png "/>Machine learning Phase-1650) this.width=650; "src=" Https

Forest 40g Maximum entropy 40g Decision Tree 40g BAGGING 40g Svm 20% Experiment two (code file Sentiment_analyse. R):Data file: http:///sentiment/data/Classification using Bayes, MAXENT, SVM, Slda, BAGGING, RF, tree classifierThe results are as follows: Classifier Name Accuracy rate (R) Accuracy rate (spark) Bayesian

Big Data Architecture Development mining analysis Hadoop HBase Hive Storm Spark Flume ZooKeeper Kafka Redis MongoDB Java cloud computing machine learning video tutorial, flumekafkastorm Training big data architecture development, mining and analysis! From basic to advanced, one-on-one training! Full technical guidance! [Technical QQ: 2937765541] Get the big da

Training Big Data architecture development, mining and analysis!from zero-based to advanced, one-to-one training! [Technical qq:2937765541]--------------------------------------------------------------------------------------------------------------- ----------------------------Course System:get video material and training answer technical support addressCourse Presentation ( Big Data technology is very wide, has been online for you training solutions!) ):Get video material and training answer

) //Decision Tree ParametersVal numclasses = 5Val Categoricalfeaturesinfo=Map[int, Int] () Val impurity= "Gini"Val maxDepth= 5Val maxbins= 32//Build a decision tree model and trainVal model =Decisiontree.trainclassifier (Trainrdd, numclasses, Categoricalfeaturesinfo, impurity, maxDepth, MaxBins) //Test the test sampleVal Predictionandlabel = Testrdd.map {point = =Val Score=model.predict (Point.features) (Score, Point.label, Point.features)} Val showpredict= Predictionandlabel.take (50) printl

Big Data Architecture Development mining analysis Hadoop Hive HBase Storm Spark Flume ZooKeeper Kafka Redis MongoDB Java cloud computing machine learning video tutorial, flumekafkastorm Training big data architecture development, mining and analysis! From basic to advanced, one-on-one training! Full technical guidance! [Technical QQ: 2937765541] Get the big da

Kmenas algorithm is relatively simple, not detailed introduction, directly on the code.Importorg.apache.log4j. {level, Logger}ImportOrg.apache.spark. {sparkconf, sparkcontext}Importorg.apache.spark.mllib.linalg.VectorsImportorg.apache.spark.mllib.clustering._/*** Created by Administrator on 2017/7/11. */Object Kmenas {def main (args:array[string]): Unit={ //setting up the operating environmentVal conf =NewSparkconf (). Setappname ("Kmeans Test"). Setmaster ("

Label:Training Big Data architecture development, mining and analysis! From zero-based to advanced, one-to-one training! [Technical qq:2937765541] --------------------------------------------------------------------------------------------------------------- ---------------------------- Course System: get video material and training answer technical support address Course Presentation ( Big Data technology is very wide, has been online for you training solutions!) ): get video material and tr

1. Basic knowledge of LDALDA (latent Dirichlet Allocation) is a thematic model. LDA a three-layer Bayesian probabilistic model that contains the word, subject, and document three-layer structures.LDA is a build model that can be used to generate a document that, when generated, chooses a topic based on a probability, then a word in the subject of probability selection, so that a document can be generated, and in turn, LDA is an unsupervised machine

First, spark environment to build 1.1 download spark Download Address: http://spark.apache.org/downloads.html After the download is complete decompression can be.Add Spark's running directory to environment variables: #Spark Home Export spark_home=/usr/local/cellar/spark-2.1.0-bin-hadoop2.7 export path= $PATH: $

[Spark] [Python] Example of a dataframe in which a limited record is taken:SqlContext = Hivecontext (SC)PEOPLEDF = SqlContext.read.json ("People.json")Peopledf.limit (3). Show ()===[Email protected] ~]$ HDFs dfs-cat People.json{"Name": "Alice", "Pcode": "94304"}{"Name": "Brayden", "age": +, "Pcode": "94304"}{"Name": "Carla", "age": +, "Pcoe": "10036"}{"Name": "Di

[Spark] [Python] An example of opening a JSON file in a dataframe way:[email protected] ~]$ cat People.json{"Name": "Alice", "Pcode": "94304"}{"Name": "Brayden", "age": +, "Pcode": "94304"}{"Name": "Carla", "age": +, "Pcoe": "10036"}{"Name": "Diana", "Age": 46}{"Name": "Etienne", "Pcode": "94104"}[Email protected] ~]$[Email protected] ~]$ HDFs dfs-put People.json

1.1 machine learning basics-python deep machine learning, 1.1-python Refer to instructor Peng Liang's video tutorial: reprinted, please indicate the source and original instructor Peng Liang Video tutorial: http://pan.baidu.com/s/

Training Big Data architecture development, mining and analysis!from zero-based to advanced, one-to-one technical training! Full Technical guidance! [Technical qq:2937765541] https://item.taobao.com/item.htm?id=535950178794-------------------------------------------------------------------------------------Java Internet Architect Training!https://item.taobao.com/item.htm?id=536055176638Big Data Architecture Development Mining Analytics Hadoop HBase Hive Storm

[Example of a limited record taken in Spark][python]dataframethe continuationIn [4]: Peopledf.select ("Age")OUT[4]: Dataframe[age:bigint]In [5]: Mydf=people.select ("Age")---------------------------------------------------------------------------Nameerror Traceback (most recent)----> 1 Mydf=people.select ("Age")Nameerror:name ' People ' is not definedIn [6]: Mydf

Python Machine Learning Theory and Practice (6) Support Vector Machine and python Learning Theory In the previous section, the theory of SVM is basically pushed down, and the goal of finding the maximum interval is finally convert

Cross-validation method thought: Crossvalidator divides the dataset into several subsets for training and testing respectively. When K=3, Crossvalidator produces 3 training data and test data pairs, each data is trained with 2/3 of the data, and 1/3 of the data is tested. For a specific set of parameter tables, Crossvalidator calculates the average of the evaluation criteria for the training model based on three sets of different training data and test data. After the optimal parameter table is

This article focuses on the contents of the 1.2Python libraries and functions in the first chapter of the Python machine learning time Guide. Learn the workflow of machine Learning.I. Acquisition and inspection of dataRequests getting dataPandans processing Data1 ImportOS2 ImportPandas as PD3 ImportRequests4 5PATH = R'

follows:#Coding=utf-8ImportSYS fromPysparkImportSparkcontext fromPysparkImportsparkconf fromPyspark.sqlImportSqlContextclassReadspark (object):def __init__(self, paramdate): Self.parquetroot='/data/parquet/%s' # Here is the HDFs pathself.thedate=paramdate self.conf=sparkconf () self.conf.set ("spark.shuffle.memoryFraction","0.5") Self.sc= Sparkcontext (appname='Readsparkdata', conf=self.conf) Self.sqlcontext=SqlContext (Self.sc)defGettypedata (self): BasePath= self.parquetroot%self.thedate Parq

[Machine learning algorithm-python implementation] matrix denoising and normalization, python Machine Learning1. The background project is required. We plan to use python to implement matrix denoising and normalization. The numpy