machine learning with python cookbook pdf

[:, 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

matrix matrices, and the column represents the feature, where the percentage represents the variance ratio of the number of features required before taking the default to 0.9" "defPCA (datamat,percentage=0.9): #averaging for each column, because the mean value is subtracted from the calculation of the covarianceMeanvals=mean (datamat,axis=0) meanremoved=datamat-meanvals#CoV () Calculating varianceCovmat=cov (meanremoved,rowvar=0)#using the Eig () method in the module linalg for finding eigen

criteria for the end of recursion are:1: All class tags are exactly the same, return the class label (this is not nonsense, all the same, the class of the hair)2: Using all the groupings or not dividing the dataset into groups that contain only unique categories, since we cannot return a unique one, then we are represented by a wave. Is our majority voting mechanism above, returning the category with the most occurrences. This is not the NPC,.The code is as follows:People can not understand the

Naive Bayesian algorithm is simple and efficient, and it is one of the first ways to deal with classification problems. With this tutorial, you'll learn the fundamentals of naive Bayesian algorithms and the step-by-step implementation of the Python version. Update: View subsequent articles on naive Bayesian use tips "Better Naive bayes:12 tips to get the Most from the Naive Bayes algorithm"Naive Bayes classifier, Matt Buck retains part of the copyri

bestfeatue in creating is:0the bestfeatue in creating are : 0{' no surfacing ': {0: ' No ', 1: {' flippers ': {0: ' No ', 1: ' Yes '}}}It is best to increase the classification function using the decision treeAlso because building a decision tree is time-consuming, because it is best to serialize the constructed tree through Python's pickle and save the object inOn the disk, and then read it when neededdef classify (Inputtree,featlabels,testvec): firststr = Inputtree.keys () [0] seconddic

), + Ss_y.inverse_transform (dis_knr_y_predict))) the Print("the average absolute error of the distance weighted K-nearest neighbor regression is:", Mean_absolute_error (Ss_y.inverse_transform (y_test), - Ss_y.inverse_transform (dis_knr_y_predict))) $ the " " the the default evaluation value for the average K-nearest neighbor regression is: 0.6903454564606561 the the r_squared value of the average K-nearest neighbor regression is: 0.6903454564606561 the Mean square error of average K nearest ne

The language used for machine learning is python. Here's how to get started with Python for "machine learning." (Environment: CentOS 7)1, two important packagesNumPy and SciPy. (http://scipy.org/scipylib/download.html) mainly deal

This article is a combination of the recommended algorithm and SVD in conjunction with machine learning combat.Any matrix can be decomposed into the form of SVD.In fact, the SVD meaning is to use the transformation of the feature space to map the data, the following will be devoted to the basic concept of SVD, first give a python, here first give a simple matrix,

), though it's no better than Microsoft's Visual Studio, but it's much more than the one that comes with it-if it's written in C, Helpless is written in Java, startup speed huge slow ~ ~Recently turned over the book "Machine Learning in Action". The book uses Python to implement some machine

Python Machine Learning Practical tutorialsShare Network address--https://pan.baidu.com/s/1miib4og Password: WTIWThe course is really good, share to everyoneMachine Learning (machines learning, ML) is a multidisciplinary interdisciplinary subject involving probability theory

1> supervised Learning (classification): First let the machine learn the sample data of each flower, and then let him according to this information, the non-marked flowers of the type of image classification.2> Characteristics: We call the results of all measurements in the data a feature.2> cross-validation: Extreme call-to-law (leave-one-out) takes a sample from the training set and trains a model on the

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

[i]) if (classifierresu Lt! = Datinglabels[i]): ErrOrcount + = 1.0 print "The total error rate is:%f"% (Errorcount/float (numtestvecs)) Print error count def img2vector (filename): Returnvect = zeros ((1,1024)) FR = open ( FileName) For I in range (+): LINESTR = Fr.readline () F or J in range (+): RETURNVECT[0,32*I+J] = Int (linestr[j]) RETURN RET Urnvectdef handwritingclasstest (): hwlabels = [] trainingfilelist = Listdir (' trainingDigits ') #load the training

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 '

Prediction problems in machine learning are usually divided into 2 categories: regression and classification .Simply put, regression is a predictive value, and classification is a label that classifies data.This article describes how to use Python for basic data fitting, and how to analyze the error of fitting results.This example uses a 2-time function with a ra