python for data science and machine learning bootcamp

!accuracy:87.07%******************* SVM ********************Training took3831. 564000s!accuracy:94.35%******************* GBDT ********************In this data set, because the cluster of data distribution is better (if you understand this database, see its T-sne map can be seen.) Since the task is simple, it has been considered a toy dataset in the deep learning

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

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

Chapter 1 of python Learning (simple examples and common data types) and python Data Types AIYQ195 learn python Chapter 1 simple examples and common data types 1. hello programs require

), 15.0*np.array (DatingLabels)) the #plt.show () - the #Unit test of Func:autonorm () the #Normmat, ranges, minvals = Autonorm (Datingdatamat) the #print (Normmat)94 #print (ranges) the #print (minvals) the the datingclasstest ()98Classifyperson ()Output:Theclassifier came back with:3, the real answer Is:3The total error rate is:0.0%Theclassifier came back with:2, the real answer Is:2The total error rate is:0.0%Theclassifier came back with:1, the real answer is:1The total error rate is:0.0%.

Python3 Learning using the APIA sample of a data structure of a dictionary type, extracting features and converting them into vector formSOURCE Git:https://github.com/linyi0604/machinelearningCode:1 fromSklearn.feature_extractionImportDictvectorizer2 3 " "4 dictionary feature Extractor:5 pumping and vectorization of dictionary data Structures6 category type feat

Environment:Win7 64-bit systemFirst step: install Python1, download python2.7.3 64-bit MSI version (here Select a lot of 2.7 of the other higher version resulting in the installation of Setuptools failure, do not know what the reason, for the time being, anyway, choose this version can be)2, install Python, all next point down.3, configure the environment variables, I am the default to add C:\Python path ca

a money lesson! 2. R language Required! So the statistical analysis of the strong push Duke Note: The shell network has a MOOC navigation site, do a good job, quite a lot of lessons have predecessors of the notes Ah, evaluation ah what, you can see. (Stop C station launched a lot of specialization, because a lot of new classes, especially capstone, and some are not open so we do not know) Yeayee. COM has a lot of examples, 3.4, suitable for beginners. Recently University of Washington's

Python data learning notes, python learning notes Data Type I. Integer and floating point number In Python, the definitions and operations of integers and floating-point numbers are the

chunk of the statistical category. for reference only. Academia in the use of MATLAB and Python bar, industry or C or Java comparison is not very clear MATLAB and Python applications for data mining in terms of the books. But I recommend Harvard CS109 this course. / http cs109.github.io/2014/ 。 It will introduce a set of

from:http://blog.csdn.net/lsldd/article/details/41551797In this series of articles, it is mentioned that the use of Python to start machine learning (3: Data fitting and generalized linear regression) refers to the regression algorithm for numerical prediction. The logistic regression algorithm is essentially regressio

, or K nearest neighbor (Knn,k-nearestneighbor) classification algorithm, is one of the simplest methods in data mining classification technology. The so-called K nearest neighbor is the meaning of K's closest neighbour, saying that each sample can be represented by its nearest K-neighbor.The core idea of the KNN algorithm is that if the majority of the k nearest samples in a feature space belong to a category, the sample also falls into this category

to recommend MIT python on The EDX platform. Data analysis: What I know is: 1. JHU's data science is a bit confusing! 2. The R language is required! Therefore, Duke statistics and analysis are strongly promoted. Note: There is a mooc navigation website under the fruit shell network flag, which is doing well. Many cou

This lesson mainly describes the processing of linear models. Including: 1. Input Representation) 2. Linear Classification) 3. Linear Regression) 4. nonlinear transformation) The author believes that to test the availability of a model, it is to use real data to do a good job. To explain how to apply linear models, the author uses linear models to solve the problem of post office data identification: Becau

processes, and finally the results are combined output. Note that the learning process here is independent of each other.There are two types of aggregations:1) After the fact: combine solutions that already exist.2) before the fact: build the solution that will be combined.For the first scenario, for the regression equation, suppose there is now a hypothetical set: H1,H2, ... HT, then:The selection principle of weight A is to minimize the errors in t

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

Original: Http://www.infoq.com/cn/news/2014/03/baidu-salon48-summaryMarch 15, 2014, in the 48th phase of Baidu Technology salon, sponsored by @ Baidu, @InfoQ responsible for organizing and implementing, from Baidu Alliance Big Data Machine Learning technology responsible for summer powder, and Sogou precision Advertising Research and development Department of tec

different features to the same interval: normalization and normalizationNormalization:From sklearn.preprocessing import MinmaxscalerStandardization:From sklearn.preprocessing import StandardscalerSelect a feature that is meaningfulIf a model behaves much better than a test data set on a training dataset, it means that the model is too fit for training data.The commonly used schemes to reduce generalization errors are:(1) Collect more training

learning: If DVC (H) is finite, gε H will be generalized (theoretically proven in Lesson 6 ). Note: generalization in Machine Learning refers to the ability to apply the rules obtained by samples to data outside the samples, that is, the gap between EIN and eout. The preceding statement has the following attributes: 1

Decision Tree Learning is one of the most widely used inductive reasoning algorithms, and is a method to approximate discrete-valued objective functions, and the functions learned in this method are represented as a decision tree. The decision tree can use unfamiliar collections of data and extract a set of rules from which the machine