Cross validation. Cross validation is used to prevent over-fitting caused by too complex a model. It is sometimes called cyclic estimation.It is a practical method to cut data samples into smaller subsets statistically.Therefore, one subset can be analyzed first, while the other subsets are used for subsequent confirmation and verification of this analysis.The initial subset is called the training set.The other subsets are called validation set or test sets.Cross-validation is to evaluate the generalization ability of statistical analysis and machine learning algorithms to data sets independent of training data.
topic: Beethoven's 2nd, a father, a mother, two sons, two daughters, and a policeman, a villain, crossed a river. The father does not hurt his son in his mother's absence, the mother does not hurt her daughter in his father's absence, and the police does not hurt Beethoven's 2nd in the villain's absence. Only Mom, Dad, and the police can sail a boat. There can only be two people and
1, training set vs test set
In the related research of pattern recognition and machine learning, the dataset is often divided into two subsets: training set and testing set. The former is used to build a model, while the latter is used to evaluate the accuracy of the model in predicting unknown samples. The normal expression is generalization ability.How to divide the complete data set into training set and test set must follow the following points:
1. What is Cross Validation? Cross-validation is a measure adopted when the data in the experiment is insufficient, but we want to train a good model.The idea of cross-validation is to reuse data, split the given data, and combine the split data sets into training sets and test sets. On this basis, training, testing and model selection are continuously repeated.The following two cross-validation methods are introduced. The cross-validation method mainly
I think the inventor of git is really a genius, not only can do it anywhere, but also can roll back any version and return to future versions.You can view the version number of each modification.You can view the modified content.
First you need to create a folder.We found git bash directly from the beginning and opened it.
$ mkdir xixixi
At this time there was xixixi's folder
1. Cross-validation When establishing the classification model, Cross Validation is simply called CV, CV is used to verify the performance of the classifier.Its main idea is to group the original data, one as training set and the other as validation set.The training set is used to train the model, and the validation set is used to test the model to evaluate the performance of the classification model. 2. Role of
IlogJRulesis the most famous commercial BRMS, just took JOLT；; Drools is the most active open source rule engine, and it has made great progress all the way.Jess is the java implementation of Clips, just as JRuby is to Ruby, and is the representative of AI system.
Today, I compared the rule languages of these three representative rule engines.Among them, Ilog is a commercial product and has no chance of actual combat.