spark machine learning example python

Sample of the data provided in the machine learning in action, which is said to be the characteristics of each candidate on a dating site, and how much the current person likes them. A total of 1k data, the first 900 as a training sample, the last 100 as a test sample.The data format is as follows:468933.5629760.445386didntlike81783.2304821.331698smalldoses557833.6125481.551911didntlike11480.0000000.332365s

It is mentioned in this series that using Python to start machine learning (3: Data fitting and generalized linear regression) mentions the regression algorithm for numerical prediction. The logical regression algorithm is essentially regression, but it introduces a logical function to help classify it. The practice found that the logical regression in the field

1. Background When I was outside the company internship, a great God told me that learning computer is to a Bayesian formula applied to apply. Well, it's finally used. Naive Bayesian classifier is said to be a lot of anti-Vice software used in the algorithm, Bayesian formula is also relatively simple, the university to do probability problems often used. The core idea is to find out the most likely effect of the eigenvalue on the result. The formula

three sheets; train_set.csv;test_set.csv;feature.csv. Three tables are associated by object_id.Copyright NOTICE: This article for Bo Master original article, without Bo Master permission not reproduced.Machine learning in Python: Merging multiple tables based on keywords (building a combined feature)

Full Stack Engineer Development Manual (author: Shangpeng) Python Tutorial Full Solution Keras uses a depth network to achieve the encoding, that is, the n-dimensional characteristics of each sample, using K as a feature to achieve the function of coding compression. The feature selection function is also realized. For example, the handwriting contains 754 pixels, and it contains 754 features, if you want t

In the model training, especially in the training set to do cross-validation, usually want to save the model, and then put on a separate test set test, the following is the Python training model to save and reuse.Scikit-learn already has the model persisted operation, the import joblib canfromimport joblibModel Save>>> Os.chdir ( "Workspace/model_save" ) >>> from sklearn import SVM >>> X = [[0 , 0 ], [1 , 1 ]]>>> y = [ 0 , 1 ]>>> CLF = SVM. SV

Python code implementation on the perception machine ----- Statistical Learning Method Reference: http://shpshao.blog.51cto.com/1931202/1119113 1 #! /Usr/bin/ENV Python 2 #-*-coding: UTF-8-*-3 #4 # Untitled. PY 5 #6 # copyright 2013 T-dofan There are still a few questions, the book's adjustment strategy is: Wi = wi

Before installing Scikit-learn, you need to install numpy,scipy. However, there are always errors when installing scipy (pip install scipy). After a series of lookups, the reason is that scipy relies on numpy and many other libraries (such as Lapack/blas), but these libraries are not easily accessible under Windows.After finding, the discovery can be solved by another way, http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpyDownload here: Numpy-1.11.2+mkl-cp34-cp34m-win32.whl Scipy-0.18.1-c

Small task: Achieve picture classification1. Picture materialPython bulk compress jpg images: PiL library resizehttp://blog.csdn.net/u012234115/article/details/502484092. Environment ConstructionInstallation version of Python under Windows comparison 2.7 vs 3.6Https://pypi.python.org/pypiInstallation of the PIL Library under WindowsHttps://pypi.python.org/pypiInstallation of the PIL Library under Windowshttp://zjfsharp.iteye.com/blog/2311523Installati

is the custom of naming in Python? I found that if the variable name was completely expanded, it would be too long-my MacBook Pro was too ugly to show up. This is followed by the variable shorthand naming of C + +.V. Entrance Call functionThe main function, similar to C + +. As soon as you run the knn.py script, the code is executed first:if __name__ = = ' __main__ ': print "You are running knn.py " CLASSIFYSAMPLEFILEBYKNN (' datingSetOne.txt '

The idea of clustering: dividing a DataSet into several subsets (called a cluster cluster) that you don't want to cross, each potentially corresponding to a concept. But the practical significance of each cluster is determined by the users themselves, and the clustering algorithm will only be divided.The role of Clustering:1) can be used as a separate process for finding a distribution pattern of data2) as a preprocessing process for classification. First, classify data is clustered and then the

[:, 1].max () + 1, 0.005 +grid_x =Np.meshgrid (Np.arange (L, R, h), A Np.arange (b, T, v)) atflat_x = Np.c_[grid_x[0].ravel (), grid_x[1].ravel ()] -Flat_y =model.predict (flat_x) -Grid_y =Flat_y.reshape (Grid_x[0].shape) -Mp.figure ('Logistic Classification', -Facecolor='Lightgray') -Mp.title ('Logistic Classification', fontsize=20) inMp.xlabel ('x', fontsize=14) -Mp.ylabel ('y', fontsize=14) toMp.tick_params (labelsize=10) +Mp.pcolormesh (Grid_x[0], grid_x[1], grid_y, cmap='Gray') -Mp.scatter

1. Background Decision Book algorithm is a kind of classification algorithm approximating discrete numbers, which is simpler and more accurate. International authoritative academic organization, Data Mining International conference ICDM (the IEEE International Conference on Data Mining) in December 2006, selected the ten classical algorithms in the field of mining, C4.5 algorithm ranked first. C4.5 algorithm is a kind of classification decision tree algorithm in

Full Stack Engineer Development Manual (author: Shangpeng) Python tutorial full solution FM problem Source CTR/CVR prediction, the user's gender, occupation, education level, category preference, commodity category, etc., after the conversion of one-hot encoding will result in sparse sample data. In particular, commodity category this type of characteristics, such as the end of the product category of about 550, using one-hot code to generate 550 nume

[:, 1].max () + 1 xx1, xx2 = Np.meshgrid (Np.arange (X1_min, X1_max, resolution), Np.aran GE (X2_min, X2_max, resolution)) Z = Classifier.predict (Np.array ([Xx1.ravel (), Xx2.ravel ()]). T) Z = Z.reshape (xx1.shape) Plt.contourf (xx1, xx2, Z, alpha=0.4, Cmap=cmap) Plt.xlim (Xx1.min (), Xx1.max ()) Plt.ylim (Xx2.min (), Xx2.max ()) # Plot all samples for IDX, C1 in enumerate (Np.unique (y)): Print Idx,c1 Plt.scatter (X=x[y = = C1, 0], Y=x[y = = C1, 1], alpha=0.8, C=cmap (IDX), MARKER=M

understand computer knowledge, psychology and philosophy. Artificial intelligence consists of a very wide range of sciences, consisting of a variety of fields, such as machine learning, computer vision, and so on, in general, one of the main goals of AI research is to make machines capable of doing complex work that normally requires human intelligence. But different times, different people's understanding

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, degree=2, Hash=hash): new_data = [] m,n = D

(labels[:: -1]) Xgtrain = XGB. Dmatrix (Train[offset:,:], Label=labels[offset:]) Xgval = XGB. Dmatrix (Train[:offset,:], label=labels[:offset]) watchlist = [(Xgtrain, ' Train '), (Xgval, ' val ')]model = Xgb.train (plst , Xgtrain, Num_rounds, watchlist, early_stopping_rounds=120) preds2 = Model.predict (xgtest,ntree_limit=model.best_ Iteration) #combine Predictions#since the metric only cares on relative rank we don ' t need to Averagepreds = (PREDS1) * *. 4 + (PREDS2) *8.6return Preds(Code fro

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

-0.576525 11.778922 0-0.346811-1.678730 1-2.124484 2.672471 11.217916 9.597015 0-0.733928 9.098687 0-3.642001-1.618087 10.315985 3.523953 11.416614 9.619232 0-0.386323 3.989286 10.556921 8.294984 11.224863 11.587360 0-1.347803-2.406051 11.196604 4.951851 10.275221 9.543647 00.470575 9.332488 0-1.889567 9.542662 0-1.527893 12.150579 0-1.185247 11.309318 0-0.445678 3.297303 11.042222 6.105155 1-0.618787 10.320986 01.152083 0.548467 10.828534 2.676045 1-1.237728 10.549033 0-0.683565-2.166125 10.229