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Rate the Fl-score the Support the 98 Logistic regression accuracy rate: 0.9707602339181286 About Other indicators of logistic regression: - Precision recall F1-score support101 102 benign 0.96 0.99 0.98103 Malignant 0.99 0.94 0.96104 the avg/total 0.97 0.97 0.97 171106 107 estimation accuracy of stochastic parameters: 0.9649122807017544108 Other indicators of stochastic parameter estimation:109 Precision recall F1-score support the 111 benign 0.97 0.97 0.97 the malignant 0.96 0.96 0.96113 th
============================================================================================ "Machine Learning Combat" series blog is Bo master reading " Machine learning Combat This book's notes, including the understanding of the algorithm and the Python code implementation of the algorithmIn addition, bloggers here
Naive Bayesian algorithm is simple and efficient, and it is one of the first ways to deal with classification problems.
With this tutorial, you'll learn the fundamentals of naive Bayesian algorithms and the step-by-step implementation of the Python version.
Update: View subsequent articles on naive Bayesian use tips "Better Naive bayes:12 tips to get the Most from the Naive Bayes algorithm"Naive Bayes classifier, Matt Buck retains part of the copyri
)]=1 else:print "The word:%s is not in my vocabulary!" %word return returnvecdef TRAINNBC (trainsamples,traincategory): Numtrainsamp=len (Trainsamples) NumWords=len (train Samples[0]) pabusive=sum (traincategory)/float (numtrainsamp) #y =1 or 0 feature Count P0num=np.ones (numwords) P1NUM=NP.O NES (numwords) #y =1 or 0 category count P0numtotal=numwords p1numtotal=numwords for I in Range (Numtrainsamp): if Traincategory[i]==1:p0num+=trainsamples[i] P0numtotal+=sum (Trainsamples[i]) E
difference from the numerical size of dimension, such as using User behavior Index to analyze the similarity or difference of user value; Cosine similarity is more to differentiate from the direction, but not sensitive to absolute values, more used to use User content scoring to distinguish the similarity and difference of user interest, and also fixed the problem that the measurement standards may exist between users (because the cosine similarity is not sensitive to absolute values). Copyrigh
OPENCV provides several classifiers, which are described by character recognition in routines.
1, Support vector Machine (SVM): Given the training samples, support vector machines to establish a hyperplane as a decision plane, so that the positive and inverse of the isolation between the edge is maximized.
Function prototype: Training prototype CV2. Svm.train (Traindata, responses[, varidx[, sampleidx[, params]])
Where Traindata is the training data,
intervals:
H is the classification surface, while H1 and H2 are parallel to h, and the line between H1 and H, H2 and H is the closest to H.Geometric Interval.
The reason why we are so concerned about the geometric interval is that the geometric interval and the sampleError countRelationships:
Delta is the interval from the sample set to the classification surface. r = max | Xi | I = 1 ,..., n, that is, r is the longest value of the vector length in all samples (Xi is the first sample represen
basically recommend II possible directions: (1) SVM ' s, or (2) Tree ensembles. If I knew nothing on your problem, I would definitely go for (2), but I'll start with describing why SVM ' s might is S Omething worth considering.Support Vector Machines Support Vectors machines (SVMs) use a different loss function (Hinge) from LR. They is also interpreted differently (maximum-margin). However, in practice, an SVM with a linear kernel was not very different from a Logistic Regression (If You are cu
This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust
Dr. Hangyuan Li's "Talking about my understanding of machine learning" machine learning and natural language processing
[Date: 2015-01-14]
Source: Sina Weibo Hangyuan Li
[Font: Big Small]
Calculating time, from the beginning to the present, do m
. According to common sense, there should be a simple tool, and then gradually improve, but the more powerful LIBSVM was released long before Liblinear. To answer this question, you have to start with machine learning and the history of SVM.
The Early machine learning classification algorithms can be traced back to th
This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust
machine learning the most powerful learning algorithm.AdaBoost is an iterative algorithm whose core idea is to train m weak classifiers for the same training set, each weak classifier assigns different weights, and then the weak classifiers are assembled to construct a stronger final
a good effect, basically do not know what method of time can first try random forest.SVM (Support vector machine)
The core idea of SVM is to find the interface between different categories, so that the two types of samples as far as possible on both sides of the surface, and the separation of the interface as much as possible.
The earliest SVM was planar and limited in size. But using the kernel function (kernel functions), we can make the plane proj
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
D4 = (0.125, 0.125, 0.125, 0.102, 0.102, 0.102, 0.065, 0.065, 0.065, 0.125).
From the above process, it can be found that if some of the samples are divided, their weights in the next iteration will be increased, while the other pairs of samples in the next iteration of the weights will be reduced. In this way, the error rate E (all the sum of the weights of the GM (x) mis-categorized samples) is always reduced by selecting the threshold value with the lowest error rates to be used to design t
is a library that recognizes and standardizes time expressions.
Stanford spied-Use patterns on the seed set to iteratively learn character entities from untagged text
Stanford Topic Modeling toolbox-is a topic modeling tool for social scientists and other people who want to analyze datasets.
Twitter text Java-java Implementation of the tweet processing library
Mallet-Java-based statistical natural language processing, document classification, clustering, theme modeling, informat
Learning notes for "Machine Learning Practice": two application scenarios of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k-
After learning the implementation of the k-Nearest Neighbor Algorithm, I tested the k-
Python Machine Learning Theory and Practice (5) Support Vector Machine and python Learning Theory
Support vector machine-SVM must be familiar with machine learning, Because SVM has alwa
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