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), 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
): # Extend the Input feature vector as a feature matrix linenum = featurematrix.shape[0] featurematrixin = Np.tile ( Featurevectorin, (linenum,1)) # Calculate the Euclidean distance between the matrix Diffmatrix = featurematrixin -Featurematrix Sqdiffmatrix = Diffmatrix * * 2 Distancevaluearray = Sqdiffmatrix.sum (Axis=1) Distancevaluearray = Distancevaluearray * * 0.5 return DistancevaluearrayUsed in the numpy of the more distinctive things. The practice is to first
, 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 and has the characteristics of the sample on
classes in the data. - -Many, many more ... the the a total of 150 data samples the evenly distributed over 3 subspecies the 4 petals per sample, calyx shape Description - " " the the " " the 2 dividing the training set and the test set94 " " theX_train, X_test, y_train, y_test =train_test_split (Iris.data, the Iris.target, thetest_size=0.25,98Random_state=33) About - " "101 3 K Nearest Neighbor Classifier learning model and prediction102 " "10
Here is still to recommend my own built Python development Learning Group: 483546416, the group is the development of Python, if you are learning Python, small series welcome you to join, everyone is the software Development Party, not regularly share dry goods (only
!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 boundary, so KNN has a good effect. GBDT is a very good algorithm, in Kaggle and other bi
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 features vectorization with 0 12 values using prototype feature names7 numeric type features r
============================================================================================ "Machine Learning Combat" series blog is Bo master reading " Machine learning Combat This book's notes, including the understanding of the algorithm and the Python code implementatio
First of all, to collect ...This article is for the author after learning Zhou Zhihua Teacher's machine study material, writes after the class exercises the programming question. Previously placed in the answer post, now re-organized, will need to implement the code to take out the part of the individual, slowly accumulate. Want to write a machine
=true) # drop useless columns and create LABELSIDX = test.id.values.astype (int) test = Test.drop ([' id ', ' tube_assembly_id ', ' quote_date '), Axis = 1) labels = Train.cost.valuestrain = Train.drop ([' Quote_date ' , ' cost ', ' tube_assembly_id '], Axis = 1) # Convert data to NumPy Arraytrain = Np.array (train) test = Np.array (test)From:kaggle Copyright NOTICE: This article for Bo Master original article, without Bo Master permission not reproduced. Ma
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
1. Confusion Matrixis a confusion matrix of two types of problems in which the output uses a different category labelCommonly used metrics to measure classification performance are:
The correct rate (Precision), which is equal to tp/(TP+FP), gives the ratio of the true positive example in the sample that is predicted to be a positive example.
recall Rate (Recall), which he equals to tp/(TP+FN),
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