choosing machine learning classifier

Naive Bayesian classifier is a set of simple and fast classification algorithms. There are many articles on the Internet, such as this one is relatively good: 60140664. Here, I'm going to sort it out as I understand it.In machine learning, we sometimes need to solve classification problems. That is, given a sample's eigenvalues (Feature1,feature2,... feauren), we

remainders graph to express the dependency between variables, variables are represented by nodes, and dependencies are represented by edges .Ancestor, parent, and descendant nodes. A node in a Bayesian network, if its parent node is known, its condition is independent of all its non-descendant nodesEach node comes with a conditional probability table (CPT)that represents the contact probability of the node and parent node Modeling stepsCreate a network structure (knowledge of hideaway industry

, here is introduced 1vs (n–1) and 1v1. More SVM Multi-classification application introduction, reference ' SVM Multi-Class classification method 'In the previous method we need to train n classifiers, and the first classifier is to determine whether the new data belongs to the classification I or to its complement (except for the N-1 classification of i). The latter way we need to train N * (n–1)/2 classifiers, the

In machine learning, the classifier function is to determine the category of a new observation sample based on the training data that is tagged with a good category. The classifier can be divided into non-supervised learning and supervised

introductionThe basic SVM classifier solves the problem of the 2 classification, the case of N classification has many ways, here is introduced 1vs (n–1) and 1v1. More SVM Multi-classification application introduction, reference ' SVM Multi-Class classification method 'In the previous method we need to train n classifiers, and the first classifier is to determine whether the new data belongs to the classif

. Optimal interval classifierThe optimal interval classifier can be regarded as the predecessor of the support vector machine, and is a learning algorithm, which chooses the specific W and b to maximize the geometrical interval. The optimal classification interval is an optimization problem such as the following:That is, select Γ,w,b to maximize gamma, while sati

special value of 0, because 0 does not affect the value update of the LR classifier.The partial deletion of sample eigenvalues in training data is a tricky issue, and many documents are devoted to solving the problem, as it is too bad to lose the data directly, and the cost of re-acquisition is expensive. Some optional data loss processing methods include:-Use the mean value of the available features to fill the missing values;-use special values to ± true complement missing values, such as-1;-

[Ai refining] machine learning 051-bag of Vision Model + extreme random forest to build an image classifier (Python library and version number used in this article: Python 3.6, numpy 1.14, scikit-learn 0.19, matplotlib 2.2) Bag of visual words (bovw) comes from bag of words (BOW) in natural language processing, for more information, see my blog [ai refining]

", Classification_report (Gbc_y_predict, Y_test, target_names=['died','survived']))103 104 " " the Single decision tree accuracy: 0.7811550151975684106 Other indicators:107 Precision recall F1-score support108 109 died 0.91 0.78 0.84 236 the survived 0.58 0.80 0.67111 the avg/total 0.81 0.78 0.79 329113 the Random forest accuracy: 0.78419452887538 the Other indicators: the Precision recall F1-score support117 118 died 0.91 0.78 0.84 237119 survived 0.58 0.80 0.68 - 121 avg/total 0.82 0.78 0.79

transformed, the ROC curve can remain unchanged. In the actual data set, the sample class imbalance often occurs, that is, the positive and negative sample ratio is large, and the positive and negative samples in the test data may change over time. Is the contrast between the ROC curve and the Presision-recall curve:In, (a) and (c) are the ROC curves, (b) and (d) are precision-recall curves.(a) and (b) show the results of classifying them in the original test set (distribution balance of positi

), i.e.Note: at this time h* is called Bayesian Optimal classifier, and the corresponding overall risk R (h*) is called Bayesian risk, when the risk is minimal, the performance of the classifier to achieve the best.Specifically, if the goal is to minimize the classification error rate, the miscalculation loss Λij can be written as:At this time the conditional risk R (c|x) =1-p (c|x), so the Bayesian optimal

classes in the data. - -Many, many more ... the the a total of 150 data samples the evenly distributed over 3 subspecies the 4 petals per sample, calyx shape Description - " " the the " " the 2 dividing the training set and the test set94 " " theX_train, X_test, y_train, y_test =train_test_split (Iris.data, the Iris.target, thetest_size=0.25,98Random_state=33) About - " "101 3 K Nearest Neighbor Classifier