andrew ng machine learning python

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

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

This section learns to use Sklearn for voting classification, see a specific example, the dataset uses the Iris DataSet, using only the sepal width and petal length two dimension features, Category we also only use two categories: Iris-versicolor and Iris-virginica, the standard uses ROC AUC.Python Machine learning Chinese catalog (http://www.aibbt.com/a/20787.html)Reprint please specify the source,

Recently learned about Python implementation of common machine learning algorithms on GitHubDirectory First, linear regression 1. Cost function2. Gradient Descent algorithm3. Normalization of the mean value4. Final running result5, using the linear model in the Scikit-learn library to implement Second, logistic regression 1. Cost funct

Full Stack Engineer Development Manual (author: Shangpeng) Python Tutorial Full solution installation Pip Install LIGHTGBM Gitup Web site: Https://github.com/Microsoft/LightGBM Chinese Course http://lightgbm.apachecn.org/cn/latest/index.html LIGHTGBM Introduction The emergence of xgboost, let data migrant workers farewell to the traditional machine learning algo

module. But this and the original SSH ratio is still not very stable, not very useful. Not suitable for production environments. To be useful or to change the native SSH, but we will not, we will only change Python. In short this chapter is to achieve a fortress machine function, really want to do a good thing to say later.The more famous is probably this: jumpserver-open-source Springboard machineLong con

~1000Importtimeitnormal_py_sec= Timeit.timeit ('sum (x*x for x in Xrange ())', number= 1000) Naive_np_sec= Timeit.timeit ('sum (na*na)', Setup="Import NumPy as Np;na=np.arange (+)", number= 1000) Good_np_sec= Timeit.timeit ('Na.dot (NA)', Setup="import NumPy as NP; Na=np.arange (+)", number= 1000)Print("Normal Python:%f sec"%normal_py_sec)Print("Naive Python:%f sec"%naive_np_sec)Print("Good NumPy:%f sec"%go

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 regression, but it introduces logic functions to help

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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

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 data(2) Introduction of penalty by regularization(3)

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

享平台来找到numpy, scipy and Matplotlib, Here are all. WHL files, which need to be installed via PIP, so there is an important preparation is easy_install pip to complete the PIP installation, after the installation is successful, it can be installed on the above three respectively. WHL for installation in Pip install **.py.5. Download the most important machine learning package: Scikit-learn, the package install

In the previous chapters, we have been using the accuracy rate (accuracy) to evaluate the performance of the model, which is usually a good choice. In addition, there are many evaluation indicators, such as precision (precision), recall rate (recall) and F1 value (F1-score).Confusion matrixBefore explaining the different evaluation indicators, let's start by learning a concept: The confusion matrix (confusion matrix), which shows the matrix of the

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

are slightly different, and many very small elements are left in the matrix, which results from the computer processing error. Enter the following command to get the error value:>>> MyEye = Randmat*invrandmat>>> Myeye-eye (4)Matrix ([[ 0.00000000e+00, -4.44089210e-16, -4.44089210e-16, -3.33066907E-16], [ -8.88178420e-16, 2.22044605e-16, 0.00000000e+00, 5.55111512E-17], [ 4.44089210e-16, 0.00000000e+00, 0.00000000e+00, -5.55111512E-17],

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,