fundamentals of machine learning for predictive data analytics

Spark Machine Learning Mllib Series 1 (for Python)--data type, vector, distributed matrix, API Key words: Local vector,labeled point,local matrix,distributed Matrix,rowmatrix,indexedrowmatrix,coordinatematrix, Blockmatrix.Mllib supports local vectors and matrices stored on single computers, and of course supports distributed matrices stored as RDD. An example of

, 0], [3, 3, 4, 0, 0, 0, 0, 2, 2, 0, 0], [5, 4, 5, 0, 0, 0, 0, 5, 5, 0, 0], [0, 0, 0, 0, 5, 0, 1, 0, 0, 5, 0], [4, 3, 4, 0, 0, 0, 0, 5, 5, 0, 1], [0 , 0, 0, 4, 0, 4, 0, 0, 0, 0, 4], [0, 0, 0, 2, 0, 2, 5, 0, 0, 1, 2], [0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0], [1, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0]]) >>> Svdrec.recommend (Mymat,1,estmethod=svdrec.svdest) the 0 and 3 similarity is:0.490950the 0 and 5 similarity is : 0.484274the 0 and Similarity is:0.512755the 1 and 3 similarity is:0.491294the 1 and 5 si

data.X = [[1.,-1., 2.], [2., 0., 0.], [0.,1.,-1.]] Binarizer= preprocessing. Binarizer (). Fit (X)#The default threshold value is 0.0PrintBinarizer#Binarizer (copy=true, threshold=0.0)Printbinarizer.transform (X)#[1.0. 1.]#[1.0. 0.]#[0.1. 0.]Binarizer= preprocessing. Binarizer (threshold=1.1)#set the threshold value to 1.1Printbinarizer.transform (X)#[0.0. 1.]#[1.0. 0.]#[0.0. 0.]4. Label preprocessing (label preprocessing)4.1) Label binary value (label binarization)Labelbinarizer is typica

attribute in the data set. The general situation is somewhere between the two.D. High-dimensional mappingMap properties to high-dimensional space. This is the most precise approach, which completely retains all the information and does not add any additional information. For example, Google, Baidu's CTR Prediction model, pre-processing will be all the variables to deal with this, up to hundreds of millions of dimensions. The benefit of this is that t

, select the most frequently occurring classification of the K most similar data as the classification of the new data. The movie category KNN analysis (image from the network) Euclidean distance (Euclidean Distance, Euclidean metric) Calculation process Diagram CaseThe code is written in Jupyter notebook. 1 ImportNumPy as NP2 ImportPandas as PD3 fromPa

Stitching raw DATA:Train_data = pd.read_csv (' train.csv ') Test_data = pd.read_csv (' test.csv ') All_data = Np.vstack ((train_data.ix[:,1:-1], TEST_DATA.IX[:,1:-1]))Merge array Vstack and Hstack functions under NumPy:>>> a = Np.ones ((2,2)) >>> B = Np.eye (2) >>> print Np.vstack ((A, b)) [[1. 1.] [1. 1.] [1. 0.] [0. 1.]]>>> Print Np.hstack ((A, b)) [[1. 1. 1. 0.] [1. 1. 0. 1.]Generate a high (2) secondary feature:def group_data (data, degr

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 guidanc

are two issues to note:1, if the data is linearly non-divided.When the data is linearly non-divided, we can also use the above method, but will come to an unacceptable solution, at this time we can detect whether the solution is valid to determine whether our data can be divided.2. What happens if W0 exists in Z?In our previous assumptions, W0 represents a const

("Training"); Trainingdata.foreach (function(item) {classifier.adddocument (Item.text, Item.label); }); varStartTime =NewDate (); Classifier.train (); varEndTime =NewDate (); varTrainingtime = (endtime-starttime)/1000.0; Console.log ("Training Time:", Trainingtime, "seconds"); Loadtestdata ();}functionLoadtestdata () {Console.log ("Loading test Data"); Fs.readfile (' Test_data.json ', ' utf-8 ',function(err,

Preface: This article describes Ng's notes about machine learning about SVM. I have also learned some SVM theories and used libsvm before. However, this time I have learned a lot about Ng's content, and I can vaguely see the process from Logistic model to SVM model. Basic Content: When using the linear model for classification, You can regard the parameter vector as a variable. If the cost function

, the choice of the first variable, in the SMO algorithm overview I also introduced, is the least satisfied with the kkt condition of this problem, kkt conditions are as follows (Kkt is relative to each sample point is (Xi,yi)):　　　　　　　　　　　　　　　　　　G (xi) if the above-mentioned formulaThe selection of the first variable is the outer loop of the SMO, and in the process of inspection, the first step is to traverse all the sample points that satisfy the 0(2) followed by the choice of the second variab

basis of Common machine learning Data mining knowledge points SSE (Sum of squared error, squared error and) SSE=∑I=1N (Xi−x⎯⎯⎯) 2 sse=\sum_{i=1}^{n} (x_i-\overline{x}) ^2 SAE (sum of Absolute error, absolute error and) sae=∑i=1n| xi−x⎯⎯⎯| sae=\sum_{i=1}^{n}| x_i-\overline{x}| SRE (Sum of Relative error, relative error and) Sre=∑i=1nxi−x⎯⎯⎯x⎯⎯⎯sre=\sum_{i=1}^{n}{

Normally to perform supervised learning you need both types of data sets: In one dataset (your ' gold standard ') you had the input data together with correct/expected output, this dataset is usual Ly duly prepared either by humans or by collecting some the data in semi-automated. But it's important, and the expected o

Netfei is a DVD leasing company. by increasing its sales by 10%, it can earn 1 million RMB in revenue, which is very impressive. How to: predict consumers' ratings for movies? (Increase the predicted value by 10 percentage points through their own systems) if the recommendations you provide to consumers are very accurate, the consumers will be very satisfied. The essence of machine learning: 1. An existin

length of 20. Now the machine has 8 GB of memory. How can this problem be solved. Iii. System Design Questions Forward maximum matching algorithm (FMM) for Chinese Word Segmentation in natural language processing ). Note: The example explains the basic idea of FMM. (1) design the data structure struct dictnote of the dictionary. (2) Use C/C ++ to implement FMM. The optional interface is Int FMM (vector He

#数据预处理方法, mainly dealing with the dimension of data and the problem of the same trend.Import NumPy as NPFrom Sklearn Import preprocessing#零均值规范Data=np.random.rand (3,4) #随机生成3行4列的数据Data_standardized=preprocessing.scale (data) #对数据进行归一化处理, that is, each value minus the mean divided by the variance is primarily used for SVM#线性数据变换最大最小化处理Data_scaler=preprocessing. M

)) +geom_point ()#加平滑模式Ggplot (Heights.weights, aes (x = Height, y = Weight)) +geom_point () +geom_smooth ()Ggplot (HEIGHTS.WEIGHTS[1:20,], AES (x = Height, y = Weight)) +geom_point () +geom_smooth ()Ggplot (heights.weights[1:200,], AES (x = Height, y = Weight)) +geom_point () +geom_smooth ()Ggplot (heights.weights[1:2000,], AES (x = Height, y = Weight)) +geom_point () +geom_smooth ()Ggplot (Heights.weights, aes (x = Height, y = Weight)) +Geom_point (AES (color = Gender, alpha = 0.25)) +Scale_al

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 guidanc

Fourth Lesson plotting Data Drawing Datat = [0,0.01,0.98];y1 = sin (2*pi*4*t);y2 = cos (2*pi*4*t);Plot (t,y1);( drawing Figure 1)Hold on; ( Figure 1 does not disappear) Plot (T,y2, ' R ');( draw in red Figure 2)Xlable (' time ') ( horizontal axis name)Ylable (' value ') ( vertical axis name)Legend (' Sin ', ' cos ')(labeled two function curves)Title (' My Plot ')Print-dpng ' Myplot.png ' ( save image)CD '/home/flipped/desktop ' Print-dpng ' myplot.png

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