python machine learning bootcamp

Efficiency comparison:It's a cliché, but this time with a new module,Run Time Test Module Timeti:1 ImportTimeit2 3normal = Timeit.timeit ('sum (x*x for x in range )', number=10000)4NATIVE_NP = Timeit.timeit ('sum (na*na)',#Repeating part5setup="import numpy as np; na = Np.arange (+)",#Setup runs only once6number=10000)#Number of repetitions7GOOD_NP = Timeit.timeit ('Na.dot (NA)',8setup="import numpy as np; na = Np.arange (+)",9number=10000)Ten One Print('Native Run time:', Normal,'\ n', A

90avg/total 0.82 0.78 0.79 329The accuracy of gradient tree boosting is 0.790273556231 Precision recall f1-score support 0 0.92 0.78 0.84 239 1 0.58 0.82 0.68 90avg/total 0.83 0.79 0.80 329Conclusion:Predictive performance: The gradient rise decision tree is larger than the random forest classifier larger than the single decision tree. The industry often uses the stochastic forest c

#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. Minmaxscaler (feature_range= (0,1)) #选定区间 (0,1), raw Data-min/(max-min)Data_scaled=data_scaler.fit (data)#数据标准化处理normalizeddata_normaliz

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,