roc curve machine learning

optimization algorithm. In the optimization algorithm, the gradient ascending algorithm is the most common one, and the gradient ascending algorithm can be simplified to the random gradient ascending algorithm.　2.2 SVM (supported vector machines) Support vectors machine:Advantages: The generalization error rate is low, the calculation cost is small, the result is easy to explain.Cons: Sensitive to parameter adjustment and kernel function selection, the original classifier is only suitable for h

], respectively, is defined as:Visually, covariance represents the expectation of the total error of two variables.If the trend of the two variables is the same, that is, if one is greater than the expected value of the other, then the covariance between the two variables is positive, and if the two variables change in the opposite direction, that is, one of the variables is greater than its own expectation, and the other one is less than its own expectation. Then the covariance between the two

Preface: This article describes Ng's notes about machine learning about SVM. I have also learned some SVM theories and used libsvm before. However, this time I have learned a lot about Ng's content, and I can vaguely see the process from Logistic model to SVM model. Basic Content: When using the linear model for classification, You can regard the parameter vector as a variable. If the cost function

method provides a method for finding the θ value of the f (θ) =0. How to maximize the likelihood function ? What is the maximum value of the first derivative at the corresponding point? (θ) to zero. So let f (θ) =? ' (θ), maximized ? (θ) can be converted to: Newton's method of seeking ? (θ) The problem of =0 Theta . The expression of the Newton method, the iterative update formula forθ is:Newton-Slavic iteration (Newton-raphson method)in the logistic regression, θ is a vector, so we generalize

second largest corresponding eigenvector (the solution to the eigenvectors are orthogonal). Which λ is our variance, also corresponds to our previous maximum variance theory, that is, to find a projection can make the most difference between the line.Matlab implementation function [Lowdata,reconmat] = PCA(data,k) [Row, col]=size(data); Meanvalue = mean (data);%vardata = var (data,1,1);Normdata = data-Repmat(Meanvalue,[Row,1]); Covmat = CoV (Normdata (:,1), Normdata (:,2));The covariance matrix

Rules for machine learning norms (two) preferences for nuclear power codes and rules[Email protected]Http://blog.csdn.net/zouxy09On a blog post, we talked about L0. L1 and L2 norm. In this article, we ramble about the nuclear norm and rule term selection.Knowledge is limited, and below are some of my superficial views, assuming that there are errors in understanding, I hope you will correct me. Thank you.Th

according to this parameter estimation and the sample calculation category distribution Q.3 The extremum of the nether function is obtained, and the parameter distribution is updated.4 iterations are calculated until convergence.Say Ah, the EM algorithm is said to be a machine learning advanced algorithm, but at least for the moment, it is still easy to understand the idea, the whole process of the only on

=[] For C in Cs: # Select Model CLS = Logisticregression (c=c) # submit data to Model training Cls.fit (X_train, Y_train) Scores.append (Cls.score (X_test, Y_test)) # # Drawing Fig=plt.figure () Ax=fig.add_subplot (1,1,1) ax.pl OT (cs,scores) ax.set_xlabel (r "C") Ax.set_ylabel (r "Score") Ax.set_xscale (' Log ') Ax.set_title ("Logisticregression") plt.show () If __name__== ' __main__ ': X_train,x_test,y_train,y_test=load_data () # Generates a dataset for regression problems Test_logist

descent algorithms such as:Description: Assigning a value to θ causes J (θ) to proceed in the quickest direction of the gradient descent, iterating all the way down and finally getting local minimum values. where α is the learning rate (learning), it determines how much we can go down in the direction that allows the cost function to fall in the most ways.For this problem, the purpose of the derivation, ba

Machine Learning (4) Logistic Regression 1. algorithm Derivation Unlike gradient descent, logistic regression is a type of classification problem, while the former is a regression problem. In regression, Y is a continuous variable, while in classification, Y is a discrete group. For example, y can only be {0, 1 }. If a group of samples is like this and linear regression is needed to fit these samples, the m

Recent job hunting really panic, on the one hand to see machine learning, on the one hand also brush code. Let's just go ahead and take a look at the course because I feel really good about it. Can ask what kind of move brick work on the fate of it.The core of this class is how to trick the kernel to the logistic regression.Firstly, the expression of relaxation variable is modified, and the form of constrai

minimum value (that is, the best fit to the data) fp1, residuals, rank, sv, rcond = sp.polyfit(x,y,1,full =True) print fp1 FP1 is a two-dimensional array with values of A and B. The printed value is [2.59619213, 989.02487106]. We obtain the linear function f (x) = 2.59619213x + 989.02487106. What is its error? Do you still remember the error function? We construct a function using the following code: f1 = sp.poly1d(fp1)print (error(f1,x,y)) We get a result: 317389767.34 is the result? Not

-Supervised learningFor supervised learning let's look at an example, which is an example of a house price forecast. The horizontal axis of the figure shows the floor space, and the ordinate indicates the price of the house transaction. Each fork in the figure represents a house instance.Now, we want to be able to predict the price of a house with a housing area of 750 square feet. The simple method is to draw an appropriate line based on the distribu

some examples of beta functions:It is of the following nature:Pareto DistributionThe Pareto principle must have heard it, is the famous long tail theory, Pareto distribution expression is as follows:Here are some examples: the left image shows the Pareto distribution under different parameter configurations.Some of the properties are as follows:ReferenceprmlmlapCopyright NOTICE: This article for Bo Master original article, without Bo Master permission not reproduced. cs281:advanced

standardize the features first. The second function, Ridgetest (), first standardizes the data, making the characteristics of each dimension equally important. You can then output a weight matrix of 30 different lambda. Then plot the ridge regression as shown in Figure 5. For the determination of Ridge regression parameters, we can use the ridge trace method, which is the lambda value taken in a place where the coefficients are relatively stable in the ridge map. You can also use the GCV genera

flow through a Relu neurons, after updating the parameters, because the activation value is too large, resulting in subsequent data activation difficult. Softmax activation function Softmax is used in multi-classification process, it will be the output of multiple neurons, mapped to the (0,1) interval, can be seen as a probability to understand, so as to carry out multi-classification! Why does the above mentioned derivative or differentiable: When updating gradients in gradient descent, yo