roc curve machine learning

Original: http://www.itongji.cn/article/06294DH015.htmlMachine learning methods are very much, but also very mature. I'll pick a few to say.the first is SVM. Because I do more text processing, so more familiar with SVM. SVM is also called Support vector machine, which maps data into multi-dimensional space in the form of dots, and then finds the optimal super-plane which can be classified, and then classifi

and polynomial regressionYou can use a custom features instead of completely copying an existing features. For example, the house has a length and width of two properties, we can create a new property--area. The expression then becomes, but this curve is reduced and then enlarged, and the actual data does not match (the larger the area, the higher the total price). So adjust to。Normal equationGradient descent gradually approximates the minimum value

9. Clustering Content 9. Clustering 9.1 Supervised learning and unsupervised learning 9.2 K-means algorithm 9.3 Optimization Objective 9.4 Random Initialization 9.5 Choosing the number of Clusters 9.1 Supervised learning and unsupervised learningWe have learned many machine

little bit at a time, and when does it get to the bottom? What happens when J (θ1) crosses the minimum to the other side if the stride is too large? These two problems have a great relationship with the learning rate α . If α is too small, the gradient descent algorithm will be quite slow. If α is too large, the gradient drop may cross the minimum, leading to non-convergence and even divergence. One thing that is comforting is that, in most of the p

ADD1 () DROP1 () 9. Regression Diagnostics Does the sample conform to the normal distribution? Normality test: function shapiro.test (X$X1) The distribution of normality Learning set/Is there outliers? How to find Outliers is the linear model reasonable? Maybe the relationship between nature is more complicated. Whether the error satisfies the independence, equal variance (the error is no

-feature quantity And for the multi-variable gradient descent algorithm, For hypothesis:hθ (x) =θtx=θ0+θ1x1+θ2x2+...+θnxn Where parameters: Θ0,θ1,..., partθn can be represented as vector θ of n+1 dimension For cost Function: The gradient descent algorithm can be transformed into: the normalization of multi-characteristic quantity If you have a machine learning problem this problem has multiple characteris

Open Course address: https://class.coursera.org/ml-003/class/index INSTRUCTOR: Andrew Ng1. unsupervised learning introduction (Introduction to unsupervised learning) We mentioned one of the two main branches of machine learning-supervised learning. Now we need to start

algorithms, there are also some algorithms that are often used to minimize the cost of functions. These algorithms are more complex and superior, and generally do not require manual learning rate, which is faster than gradient descent algorithms. These include:Bounded gradient(Conjugate gradient ),Local Optimization Method(Broyden Fletcher Goldfarb shann, BFGS) andLimited Memory Local Optimization Method(Lbfgs ). These algorithms have an intelligent

python is an object-oriented, interpretive computer programming language with a rich and powerful library, coupled with its simplicity, ease of learning, speed, open source free, portability, extensibility, and object-oriented features,python Become the most popular programming language of the 2017! AI is one of the hottest topics, and machine learning technolog

the iterative speed of this method!　　Advantages: Global optimal solution, easy parallel implementation;　　Cons: The training process is slow when the number of samples is large. From the number of iterations, the number of BGD iterations is relatively small. The convergence curve of its iteration can be expressed as follows:　　　　　　　 　　　　　　　 2, small batch gradient descent method MbgdIn the way of the batch gradient above, all samples are used for each

BoostingBoosting in training will give a weight to the sample, and then make the loss function as far as possible to consider those sub-error class samples (such as to the sub-class of the weight of the sample to increase the value)Convex optimizationThe optimal value of a function is often solved in machine learning, but in general, the optimal value of any function is difficult to solve, but the glo

, the prediction of the target and predicted values are also characterized 4) Data training, Select a model to process data: such as decision tree model, random forest model 5) algorithm validation, model tuning and parameter optimization, learning curve analysis Python ml biosphere 1) numpy/scipy basic data structure and common statistical methods 2) Scikit-learn algorithm for all feature matrix

), done, q (z) is P (zi|xi), or write P (zi), is one thing, representing the first I data is the probability from Zi.So the EM algorithm comes out, and it does this:First, initialize the parameter θ(1) E-step: Calculate the probability that each sample belongs to Zi according to the parameter θ, that is, the probability that this height comes from Sichuan or northeast, this probability is Q(2) M-step: According to the calculated Q, the lower bound of the likelihood function containing θ is obtai

Machine Learning (5): Newton algorithm 1. Introduction to Newton Iteration Algorithm set R as the root, and use it as the initial approximation of R. DoCurve The tangent L and l equations are used to obtain the abscissa between the intersection of L and the X axis, and X1 is an approximate value of R. The point is used as the tangent of the curve, and the absciss

(decision boundary) is equivalent to the original linear regression3.1 Parametric SolutionAfter the mathematical form of the model is determined, the rest is how to solve the parameters in the model. One of the most common methods in statistics is the maximum likelihood estimation, which is to find a set of parameters, so that the likelihood value (probability) of our data is greater under this set of parameters. In a logistic regression model, the likelihood value can be expressed as:Logarithm

best choice. The source of the contradiction here is the past-fitting situation mentioned earlier. Figure 3 Visualization of linear regression So, what's the most intuitive way to see if there's ever a fit? Of course, it's drawing. # draw the corresponding image Plt.scatter (x, Y, c= "G", s=20) for D in Test_set: plt.plot (x0, Get_model (d) (), label= "degree = {}" . Format (d)) # Limit the range of the horizontal and vertical axes to ( -2,4), (〖10〗^5,8x〖10〗^5) Plt.xlim ( -2, 4) Plt.ylim (

A machine learning tutorial using Python to implement Bayesian classifier from scratch, python bayesian The naive Bayes algorithm is simple and efficient. It is one of the first methods to deal with classification issues. In this tutorial, you will learn the principles of the naive Bayes algorithm and the gradual implementation of the Python version. Update: see The subsequent article "Better Naive Bayes: 1

Norm rule in machine learning (II.) kernel norm and rule item parameter selection[Email protected]Http://blog.csdn.net/zouxy09In the previous blog post, we talked about the l0,l1 and L2 norm, which we ramble about in terms of nuclear norm and rule parameter selection. Knowledge is limited, the following are some of my superficial views, if the understanding of the error, I hope that everyone to correct. Tha

Machine Learning: this paper uses the analysis of the taste of red wine as an example to describe the cross-validation arbitrage model. The least squares (OLS) algorithm is commonly used in linear regression. Its core idea is to find the best function matching of data by minimizing the sum of squares of errors. However, the most common problem with OLS is that it is easy to over-fit: that is, the attribute

Fourth Lesson plotting Data Drawing Datat = [0,0.01,0.98];y1 = sin (2*pi*4*t);y2 = cos (2*pi*4*t);Plot (t,y1);( drawing Figure 1)Hold on; ( Figure 1 does not disappear) Plot (T,y2, ' R ');( draw in red Figure 2)Xlable (' time ') ( horizontal axis name)Ylable (' value ') ( vertical axis name)Legend (' Sin ', ' cos ')(labeled two function curves)Title (' My Plot ')Print-dpng ' Myplot.png ' ( save image)CD '/home/flipped/desktop ' Print-dpng ' myplot.png ' ( save image to desktop)Close(image off)La