udacity machine learning nanodegree review

post-pruning algorithm (its disadvantage is that it is computationally large), with the minimum expected cost of miscalculation (ECM) and the minimum description length (DML) algorithm. A post-pruning algorithm is described below, which determines whether to merge leaf nodes based on the test data and the error size:Split the test data for the given tree:If The Eithersplit is a tree:call prune on that splitCalculate theerror associated with merging leaf nodesCalculate Theerror without mergingIf

can be processed.Cons: Easy to fit.How to avoid overfitting:(1) dimensionality reduction, can use PCA algorithm to reduce the dimension of the sample, so that the number of theta of the model is reduced, the number of times will be reduced, to avoid overfitting;(2) regularization, the design of regular items regularization term.The regularization function is to prevent some properties before the coefficient weight is too large, there has been a fitting.Note that the way to resolve overfitting i

A survey of data cleansing and feature processing in machine learning with the increase of the size of the company's transactions, the accumulation of business data and transaction data more and more, these data is the United States as a group buying platform of the most valuable wealth. The analysis and mining of these data can not only provide decision support for the development direction of the American

Decision tree is to select the most information gain properties, classification.The core part is to use information gain to judge the classification performance of attributes. The information gain is calculated as follows:Information entropy:Multiple categories are allowed.Calculates the information gain for all attributes, choosing the largest root node as the decision tree. Then, the sample branches, continuing to determine the remaining properties of the information gain.Information gain has

The idea of boosting is to integrate learning and combine many weak classifiers to form a strong classifier.First enter the original training sample, get a weak classifier, you can know its correct rate and error rate. Calculate the weight of the weak classifier as follows:Then increase the weight of the error classification sample, let the following classifier focus them, adjust the weight of the sample:If the original classification is correct:If th

general, multiplication does not satisfy the exchange law: $ \ Matrix {A} \ times \ matrix {B} \ not = \ matrix {B} \ times \ matrix {A} $Special Matrix $ \ Matrix {I }=\ matrix {I _ {n \ times N }}=\ begin {bmatrix} 1 0 \ cdots 0 0 \ Cr0 1 \ cdots 0 0 \ Cr \ vdots \ vdots \ Cr0 0 \ cdots 1 0 \ Cr0 0 \ cdots 0 1 \ Cr \ end {bmatrix} $ For any matrix $ \ matrix {A} $: $ \ Matrix {A} \ times \ matrix {I }=\ matrix {I} \ times \ matrix {A }=\ matrix {A} $Inverse Matrix and inverte

Logistic regression is used to classify, and linear regression is used to return.Linear regression is the addition of the properties of the sample to the front plus the coefficients. The cost function is the sum of squared errors. Therefore, in the minimization of the cost function, you can directly derivative, so that the derivative equals 0, as follows:Gradient descent can also be used to learn the same gradient as the logistic regression form.Advantages of linear regression: simple calculatio

Naive Bayesian algorithm is to look for a great posteriori hypothesis (MAP), which is the maximum posteriori probability of the candidate hypothesis.As follows:In Naive Bayes classifiers, it is assumed that the sample features are independent from one another:Calculate the posterior probability of each hypothesis and choose the maximum probability, and the corresponding category is the result of the sample classification.Advantages and DisadvantagesVery good for small-scale data, suitable for mu