roc curve machine learning

Octave Machine Learning Common commands A, Basic operations and moving data around 1. Attach the next line of output with SHIFT + RETURN in command line mode 2. The length command returns a higher one-dimensional dimension when apply to the matrix 3. Help + command is a brief aid for displaying commands 4. doc + command is a detailed help document for displaying commands 5. Who command displays all current

you're going to study machine learning, or build a machine learning application, the best practice is not to build a very complex system, to have a complex variable, but to construct a simple algorithm so you can implement it quickly.Whenever I study machine

solved. Hope Advice4, How to optimize the model? How to evaluate the model good or bad? A: Model optimization mainly from the data and model two aspects, according to specific problems, such as over-fitting and too little data volume can be considered to increase the amount of data. model evaluation indicators include classification and regression , classification such as accuracy rate ,AUC value , or business-related weighted calculation formula. It is emphasized that the AUC value of the

Twitter-text-Rb-this library can automatically connect and extract user names, lists, and topic tags from Twitter. General Machine Learning Ruby machine learning-some machine learning algorithms implemented by ruby.

), W is also a row vector of size m, and B is a constant):On a two-dimensional plane, the above demarcation is a straight line, such as a line separating black and white dots. The demarcation of the three-dimensional plane will be a plane, and the higher dimensional plane will be the other demarcation forms, so this demarcation is called the super-plane (Hyper plane).Figure 1Again, we assume that the distribution of statistical samples is evenly distributed, so that in two classification categor

implemented by ruby. Machine Learning Ruby Jruby mahout-Excellent! Apache mahout has been released in the jruby world. Cardmagic-classifier-common classifier modules available for Bayesian and other classification methods. Neural Networks and deep learning-sample code in neural networks and deep learning. Data ana

, the best practice is not to build a very complex system, to have a complex variable, but to construct a simple algorithm so you can implement it quickly. Whenever I study machine learning problems, I only spend a day, literally 24 hours, trying to get the results out quickly, even if they're not working. Frankly speaking, there is no complicated system at all, but it is just a quick result. Even if the op

2.3 Performance Metrics2.3.1 Performance metrics in a regression taskmean square error2.3. Performance metrics in the 2 classification tasksAccuracy acc = (TP+TN)/(TP+FN+FP+TN) error rate E =(FN+FP)/(TP+FN+FP+TN)Accuracy P = tp/(TP+FP) The proportion of the melon picked out by the good melon is the recall rate R = tp/(TP+FN) = tp/m+ The ratio of good melons to all the good melons picked out.True sample Rate TPR = tp/(TP+FN) =tp/m+ The proportion of all positive cases selectedFalse positive c

of the total number of features with non-0 weights)9. Logistic regression : Two-dollar category, extremely efficient Giallo Computer System (many problems need to use probability estimates as output) two ways: "As is" "converted to two-dollar category" Application: Automatic diagnosis of disease (to investigate the risk factors that cause disease, and to predict the probability of disease occurrence according to risk factors), economic forecasts and other fieldsCategory: Evaluation indicators:

Introduction to machine learning--talking about neural network This article transferred from: http://tieba.baidu.com/p/3013551686?pid=49703036815see_lz=1#Personal feel is very full, especially suitable for contact with neural network novice. Start with the question of regression (Regression). I have seen a lot of people say that if you want to achieve strong AI, you have to let the

under-fitting with verification curveValidating a curve is a very useful tool that can be used to improve the performance of a model because he can handle fit and under-fit problems.The verification curve and the learning curve are very similar, but the difference is that the accuracy rate of the model under different

code and support content for the Ruby Manor demo of data visualizationRuby-plot-Gnuplot is packaged in ruby form, ideal for converting ROC curves to SVG filesPLOT-RB-Ruby Drawing library based on Vega and D3Scruffy-rubyThe Great Graphics Toolkit of the holy crowd-bag freelancers platform (http://www.dashengzb.cn/)ScirubyGlean-Data management ToolsBiorubyArelMiscMiscellaneousBig Data for chimps-Big data processing a serious and interesting guide bookS

1. Confusion Matrixis a confusion matrix of two types of problems in which the output uses a different category labelCommonly used metrics to measure classification performance are: The correct rate (Precision), which is equal to tp/(TP+FP), gives the ratio of the true positive example in the sample that is predicted to be a positive example. recall Rate (Recall), which he equals to tp/(TP+FN), gives the true positive example of the predicted positive example as the proportion of al

select the cost parameter C (http://cran.r-project.org/web/packages/svmpath/index.html) of the support vector machine. The ROCR package provides functions for visualizing the performance of the classifier, such as the ROC Curve (http://cran.r-project.org/web/packages/ROCR/index.html). The caret package provides a variety of functions for establishing predictive

added in order to prevent a conditional probability of 0, thus the whole P (x| Y) = P (x1| Y) P (x2| Y) ... P (xn| Y) product is 0. Instance Demo classifies a message as a data collection from the University of California, Irvine, http://archive.ics.uci.edu/ml/datasets/spambase Spambase data Format description is as follows: English, I do not explain (mainly to explain the difficulty,-_-. Sorry to determine the quality of the classification model, you can calculate the AUC value. This exp

when you get to the 400-step iteration, the curve looks pretty flat. Which means that the gradient descent algorithm is basically converging here in the 400-step iteration because the cost function does not continue to fall so look at this curve can help you determine whether the gradient descent algorithm has been convergent by the way, the number of iterations required for each particular problem gradien

-dimensional plane, the above demarcation is a straight line, such as a line separating black and white dots. The demarcation of the three-dimensional plane will be a plane, and the higher dimensional plane will be the other demarcation forms, so this demarcation is called the super-plane (Hyper plane).Figure 1Again, we assume that the distribution of statistical samples is evenly distributed, so that in two classification categories (category 1 or 1) you can set the threshold to 0. In the actua

PremiseThis series of articles is not intended to be used to study the derivation of mathematical formulae, but to quickly implement the idea of machine learning in code. The main thing is to comb your thoughts.Perception MachineThe perception machine is to accept the data transmitted by each sensory element (neuron), which will produce corresponding behavior whe

square feet size house to sell, then based on the above data, how do you speculate about how much the house worth. For this problem, we can apply the machine learning algorithm, draw a line in this set of data or a line of quasi-unity, according to this line we can speculate that the house may sell $000. Of course, this is not the only algorithm, such as a two-time function may be more suitable for the exi

Stanford University's Machine learning course (The instructor is Andrew Ng) is the "Bible" for learning computer learning, and the following is a lecture note.First, what is machine learningMachine learning are field of study that