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classes more equal. but .....Recall, though,that better data often beats better algorithms, and designing good features goes a long. And if you had a huge dataset, your choice of classification algorithm might not really matter so much in terms of Classi Fication performance (so choose your algorithm based on speed or ease of use instead).And if you really-accuracy, you should definitely try a bunch of different classifiers and select the best one by
SAS graphical user interfaces help you build machine-learning models and implement an iterative machine learning process. You don ' t have a advanced statistician. Our comprehensive selection of the machine learning
Nine algorithms for machine learning---naive Bayesian classifierTo understand the Naive Bayes classificationBayesian classification is a generic term for a class of classification algorithms, which are based on Bayesian theorem, so collectively referred to as Bayesian classification. Naive naive Bayesian classification
hope for in the earthquake prediction is that the recall is very high, that is to say, every earthquake we want to predict. We can sacrifice precision at this time. 1000 alarms are preferred, 10 earthquakes are predicted correctly, and do not predict 100 times 8 leaks two times.
Suspects convictedBased on the principle of not blaming a good man, we hope to be very accurate about the conviction of a suspect. In time, some criminals were spared (recall low), but also worthwhile.
Regressi
Perception Machine: This is the simplest machine learning algorithm, but there are a few points to note. The first is the selection of the loss function, and in order to minimize the loss function, the gradient descent method used in the iterative process, finally obtains the optimal w,bThe visual interpretation is to adjust the value of the w,b, so that the sepa
A simple introduction to machine learning algorithms.As the team (Big Data Team) technology development needs, through the traffic business data needs to expand, to achieve data mining and data analysis technology mastery, bypassing the machine learning algorithm, it can be said that the core value of big data lies in
. Or after the derivation of the formula can not be interpreted, or the number of unknown parameters is greater than the number of equations. At this point, the iterative algorithm is used to find the optimal solution step-after-step.
In particular, if the optimization function is a convex function, then there is a global optimal solution, if the function is non-convex, then there will be many local optimal solutions, so the importance of convex optimization is self-evident. People always wan
]) *double (Dy[i])#Sqx = double (Dx[i]) **2Sumxy= VDOT (Dx,dy)#returns the point multiplication of two vectors multiplySQX = SUM (Power (dx,2))#Square of the vector: (x-meanx) ^2#calculate slope and interceptA = sumxy/SQXB= meany-a*MeanxPrintA, b#Draw a graphicPlotscatter (XMAT,YMAT,A,B,PLT)7.1.4 Normal Equation Group methodCode implementation of 7.1.5 normal equation set#data Matrix, category labelsXarr,yarr = Loaddataset ("Regdataset.txt")#Importing Data Filesm= Len (Xarr)#generate x-coordinat
value of 3.For example: Np.random.randint (3, 6, size=[2,3]) returns data with a dimension of 2x3. The value range is [3,6].(4). Random_integers (low[, high, size]), similar to the above randint, the difference between the range of values is closed interval [low, high].(5). Random_sample ([size]), returns the random floating-point number in the half-open interval [0.0, 1.0]. If it is another interval [a, b), it can be converted (b-a) * Random_sample ([size]) + AFor example: (5-2) *np.random.ran
') plt.ylabel (' Ratio_sugar ') plt.title (' LDA ') plt.show () W=calulate_w () plot (W)The results are as follows: The corresponding W value is:[ -6.62487509e-04, -9.36728168e-01]Because of the relationship between data distribution, LDA's effect is not obvious. So I changed the number of samples of several label=0, rerun the program to get the result as follows:The result is obvious, the corresponding W value is:[-0.60311161,-0.67601433]Transferred from: http://cache.baiducontent.com/c?m= 9d7
In the process of machine learning, we often meet the problem of fitting. The high dimension of input data or features is one of the problems that lead to overfitting. The higher the dimension, the more sparse your data will be in each feature dimension, which is basically catastrophic for machine learning
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
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 curve is above the Curve.The common convex functions are:
exponential function f (x) =ax;a>1
Negative logarithm function? logax;a>1,x>0
Two-time function of opening up
The decision of the convex function:1, If F is a first-order, x, y in any data domain satisfies F (y) ≥f (x) +f′ (x) (y?x)2. If f is a differentiable guide,Examples of convex optimization applications
SVM: which consists of max|w| Turn min (12?| W|2)
Least squares?
The loss function of L
Nine algorithms for machine learning---regressionTransferred from: http://blog.csdn.net/xiaohai1232/article/details/59551240Regression analysis is to quantify the size of the dependent variable affected by the independent variable, to establish a linear regression equation or a nonlinear regression equation, so as to predict the dependent variable, or the interpr
training samples.The above two or three can be done in the case of inverse existence, but what if the characteristics of the data are more than the sample points, because the inverse is not present at this time? You can use the ridge regression method to solve this problem, that is, it will be converted to, the other and the previous approach is similar.Of course, there is a method called forward stepwise regression, it is through each step to a certain weight increase or decrease a small value
One of the top ten algorithms for Machine Learning: EM algorithm. One of the top 10, which makes people think Nb-rich. What is Nb? We generally say someone is Nb because he can solve problems that others cannot solve. Why God is God, because God can do things that many people cannot do. So what problems can the EM algorithm solve? Or the reason why the EM algorit
The idea of clustering: dividing a DataSet into several subsets (called a cluster cluster) that you don't want to cross, each potentially corresponding to a concept. But the practical significance of each cluster is determined by the users themselves, and the clustering algorithm will only be divided.The role of Clustering:1) can be used as a separate process for finding a distribution pattern of data2) as a preprocessing process for classification. First, classify data is clustered and then the
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