udacity python machine learning

Rate the Fl-score the Support the 98 Logistic regression accuracy rate: 0.9707602339181286 About Other indicators of logistic regression: - Precision recall F1-score support101 102 benign 0.96 0.99 0.98103 Malignant 0.99 0.94 0.96104 the avg/total 0.97 0.97 0.97 171106 107 estimation accuracy of stochastic parameters: 0.9649122807017544108 Other indicators of stochastic parameter estimation:109 Precision recall F1-score support the 111 benign 0.97 0.97 0.97 the malignant 0.96 0.96 0.96113 th

) Seeking a=x *θ (2) Ask E=g (A)-y(3) Request (A for step)3, algorithm optimization--stochastic gradient methodThe gradient rise (descent) algorithm needs to traverse the entire data set each time the regression coefficients are updated, which is good when dealing with about 100 datasets, but if there are billions of samples and thousands of features, the computational complexity of the method is too high. An improved method is to update the regression coefficients with only one sample point at

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(i) Understanding decision Trees1, decision tree Classification principleRecent surveys have shown that decision trees are also the most frequently used data mining algorithms, and the concept is simple. One of the most important reasons why a decision tree algorithm is so popular is that the user does not have to understand the machine learning algorithm, nor does it have to delve into how it works. Intuit

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

is, the distribution statistics of the numbers appear, and are the result of normalization to the 0~1 interval. That is, the horizontal axis represents the number, and the vertical is the percentage of the number that corresponds to the horizontal axis in the 1000 random numbers. If you do not use the normalized horizontal axis for numbers (Normed=false), the vertical axis indicates the number of occurrences. If normalization is not used--the longitudinal axis indicates the number of oc

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

[Machine Learning Algorithm Implementation] Principal Component Analysis (PCA)-based on python + numpy, pcanumpy[Machine Learning Algorithm Implementation] Principal Component Analysis (PCA)-based on python + numpy @ Author: wepon

Spark Machine Learning Mllib Series 1 (for Python)--data type, vector, distributed matrix, API Key words: Local vector,labeled point,local matrix,distributed Matrix,rowmatrix,indexedrowmatrix,coordinatematrix, Blockmatrix.Mllib supports local vectors and matrices stored on single computers, and of course supports distributed matrices stored as RDD. An example of

At present, machine learning is one of the hottest technologies in the industry.With the rapid development of computer and network, machine learning plays a more and more important role in our life and work, and it is changing our life and work. From the daily use of the camera, daily use of the search engine, online e

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

Boring, adapt to the trend, learn the Python machine learning it.Buy a book, first analyze the catalogue it.1. The first chapter is the Python machine learning ecosystem.1.1. Data science or m