roc curve machine learning

Objective Machine learning is divided into: supervised learning, unsupervised learning, semi-supervised learning (can also be used Hinton said reinforcement learning) and so on. Here, the main understanding of supervision and unsu

Original: http://blog.csdn.net/abcjennifer/article/details/7797502This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization, neural network, design of the computer learning system, SVM (Support vector machines), clustering, dimensionality reduc

Stanford University machine Learning lesson 10 "Neural Networks: Learning" study notes. This course consists of seven parts: 1) Deciding what to try next (decide what to do next) 2) Evaluating a hypothesis (Evaluation hypothesis) 3) Model selection and training/validation/test sets (Model selection and training/verification/test Set) 4) Diagnosing bias vs. varian

Machine learning system Design (Building machines learning Systems with Python)-Willi Richert Luis Pedro Coelho General statementThe book is 2014, after reading only found that there is a second version of the update, 2016. Recommended to read the latest version, the ability to read English version of the proposal, Chinese translation in some places more awkward

Application Recommendations for machine learningFor a long time, the machine learning notes have not been updated, the last part of the updated neural network. This time we'll talk about the application of machine learning recommendations.Decide what to do nextSuppose we nee

curve to fit the data to avoid the occurrence of overfitting and under-fitting phenomenon.Training and testingWe trained to get a model, here is the two curves we fit. In order to verify the accuracy of our training model, we can take part of the training data and use it as test data during the initial training, and not only judge the model by the approximation error.SummarizeThis section is introduced as a small experiment of

understand;Each has its advantages and disadvantages, and in the case of a single model,——————————————————————————Related content:1. R Language ︱roc Curve--performance evaluation of classifier2. Problem of overfitting in machine learning3. R Language ︱ machine learning Mode

automatically connect and extract user names, lists, and topic tags from Twitter. 11.2 Machine Learning Some of the machine learning algorithms implemented by Ruby machines Learning-ruby. Machine

WEEK1:Machine learning: A computer program was said to learn from experience E with respect to some class of tasks T and performance measure P, if Its performance on tasks in T, as measured by P, improves with experience E. Supervised learning:we already know what we correct output should look like. Regression:try to map input variables to some continuous function.

The motive and application of machine learningTools: Need genuine: Matlab, free: Octavedefinition (Arthur Samuel 1959):The research field that gives the computer learning ability without directly programming the problem.Example: Arthur's chess procedure, calculates the probability of winning each step, and eventually defeats the program author himself. (Feel the idea of using decision trees)definition 2(Tom

+TN)). ROCthe curve is given when the threshold valueChanges in the rate of false yang and Zhenyang. The lower-left point corresponds to the case where all samples are judged as counter-cases, and the upper-rightThe point of the corner corresponds to the case where all samples are judged as positive cases. The dashed line gives the result curve of the random guess. ROCthe

some time ago on the Internet to see the Coursera Open Classroom Big Machine learning Cornerstone Course, more comprehensive and clear machine learning needs of the basic knowledge, theoretical basis to explain. There are several more important concepts and ideas in foundation, first review, and then open the follow-up

Machine learning system Design (Building machines learning Systems with Python)-Willi Richert Luis Pedro Coelho General statementThe book is 2014, after reading only found that there is a second version of the update, 2016. Recommended to read the latest version, the ability to read English version of the proposal, Chinese translation in some places more awkward

Public Course address:Https://class.coursera.org/ml-003/class/index INSTRUCTOR:Andrew Ng 1. Learning with large datasets ( Big Data Learning ) The importance of data volume has been mentioned in the previous lecture on machine learning design. Remember this sentence: It is not who has the best algorithm that w

the loss size corresponding to each value in the verification set, and select the smallest one. 5. Learning Curve High Bias: JCV and jtrain are both very high when m is large. In this case, increasing the number of samples does not work. Because the model itself has a problem. The possible problem is that the model is too simple. High variance: The interval between JCV and jtrain is large. In this cas

), Recall (recall rate), accuracy (accuracy), F-score (F-Score), Roc Curve (ROC Curve), AUC (AUC area), Liftcurve (lift curve), KS Curve (KS curve).PGM (Probabilistic graphical models p

(ROC Curve), AUC (AUC area), Liftcurve (lift curve), KS Curve (KS curve).PGM (Probabilistic graphical models probability map model):BN (Bayesian Network/bayesian belief network/beliefnetwork Bayesian network/Bayesian Reliability Network/Belief network), MC (Markov Chain Mar

Curve (ROC Curve), AUC (AUC area), Liftcurve (lift curve), KS Curve (KS curve).PGM (Probabilistic graphical models probability map model):BN (Bayesian Network/bayesian belief network/beliefnetwork Bayesian network/Bayesian Reliab

(ROC Curve), AUC (AUC area), Liftcurve (lift curve), KS Curve (KS curve).PGM (Probabilistic graphical models probability map model):BN (Bayesian Network/bayesian belief network/beliefnetwork Bayesian network/Bayesian Reliability Network/Belief network), MC (Markov Chain Mar

), accuracy (accuracy), F-score (F-Score), Roc Curve (ROC Curve), AUC (AUC area), Liftcurve (lift curve), KS Curve (KS curve).PGM (Probabilistic graphical models probability map model):