advanced machine learning with scikit learn

,ytest) is the 15th question only needs to carry on the operation, for the brevity all writes together.#include "stdafx.h" #include (3) Answer: last item. In fact, with the brain to know is the last one, should be f (x1,x2) =sign (x1^2+x2^2-0.6) is a circle, then the obtained affirmation is almost a circle. Plus the noise can deviate slightly from the original circle, but not too much.15. Question 15th(1) Test instructions: On the basis of the optimal W obtained in 14, we generate 1000 test samp

Today we share the coursera-ntu-machine learning Cornerstone (Machines learning foundations)-exercise solution for job three. I encountered a lot of difficulties in doing these topics, when I find the answer on the Internet but can not find, and Lin teacher does not provide answers, so I would like to do their own questions on how to think about the writing down,

Reprint Please specify source: http://www.cnblogs.com/ymingjingr/p/4271742.htmlDirectory machine Learning Cornerstone Note When you can use machine learning (1) Machine learning Cornerstone Note 2--When you can use

Hello everyone, I am mac Jiang, today and you share the coursera-ntu-machine learning Cornerstone (Machines learning foundations)-job four of the exercise solution. I encountered a lot of difficulties in doing these topics, when I find the answer on the Internet but can not find, and Lin teacher does not provide answers, so I would like to do their own questions

Feedforward network, for example, we look at the typical two-layer network of Figure 5.1, and examine a hidden-layer element, if we take the symbol of its input parameter all inverse, take the tanh function as an example, we will get the opposite excitation function value, namely Tanh (−a) =−tanh (a). And then the unit all the output connection weights are reversed, we can get the same output, that is to say, there are two different sets of weights can be obtained the same output value. If ther

= 0.04514-15: The question of 第14-15(1) Test instructions: 14. Take LAMDA value respectively. Calculate Ein and Eout. Choose the correct answer for the smallest ein, and if the answer is two lambda, select a large lambda15. Select the correct answer for the minimum eout(2) Answer: 14.log =-8, Ein = 0.015,eout = 0.0215.log = -7,ein = 0.03,eout = 0.01516. Question 16th(1) Test instructions: Using the first 120 samples as a training sample, the last 80 samples as test samples, respectively, calcul

lambda obtained from 17, the whole sample is used as the training sample. Calculate Ein,eout(2) Answer: Ein = 0.035 eout=0.0219-20: The question of 第19-20(1) Test instructions: 19. Divide the sample into 5 parts, calculate the ECV by the method of cross-validation, calculate the minimum ecv20. Calculate ein,eout with the corresponding lambda value for the minimum ecv obtained by 19(2) Answer: 19. Log=-8, Eval = 0.0320.Ein = 0.015. Eout = 0.02The source of this article: http://blog.csdn.net/a101

first, the integration method(Ensemble Method)The integration approach mainly includesBaggingand theboostingtwo methods,the random forest algorithm is based onBaggingthe idea of machine learning algorithms,in theBaggingin this method, the training data sets are sampled randomly to regroup different datasets, the weak learning algorithm is used to study different

learning of a few more difficult to learn the training samples to learn, so as to get a predictive function sequence, each of whichhave a weight that predicts a good predictor function with a larger weight. The final predictive function can be used in two ways for classification and regression problems: Classification problem: The right to vote in a h

by implementing algorithms that are able to learn from the data that they has E, machine learning technologies already outperform traditional analytics by far. (No wonder high-flying companies like Google, LinkedIn, Amazon and Pandora have built their businesses around it .) the key is the ability of machines to independently assess patterns and outcomes withi

and is easily downloaded and modified by the reader.The following books will not be introduced, share the graphic coverHere is still to recommend my own built Python development Learning Group: 725479218, 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 Python software develo

the parent delegation model, where a classloader receives a request for class loading, is first delegated to the parent ClassLoader to complete, all load requests are routed to the top-level startup ClassLoader, and if the parent ClassLoader feedback fails to complete the load request, it continues to be loaded by the subclass.The benefit of this load is that the Java class has a hierarchical relationship with precedence over its classloader, avoiding the occurrence of a class loaded multiple t

A simple and easy-to-learn machine learning algorithm--EM algorithmThe problem of parameter estimation in machine learningIn the previous blog post, such as the "easy-to-learn machine learning