data mining practical machine learning tools and techniques

. "We see very clearly from the experiment that the translation capabilities of the NIC are improved due to the increase in the data set." "The Google team said.is an example of a group of image translation results-grouped by translation results score:Obviously, this is another project in the near future in which machines will surpass human beings. Google original thesis title: Show and Tell:a neural imagecaption GeneratorPaper Link: arxiv.org/abs/141

Outer class is placed in the scope of the handle of the function. When the compiler looks forInner2class, the name of the definition used in theInner2Classes andOuterall the names in the class are in scope. the use of Val (before the declaration of today's Val ) is correct: Bind the reference to a data member in the Inner2 class[I don't understand what this paragraph means .%>_。 SameInner2::p rocessin the member function bodyOuterClass ofHandleAre al

resultIf it is an engineering program, consider here if the error rate=0 case, do a special deal.In the end, Lin theoretically discussed the basis of AdaBoost:Why does this approach work?1) The Ein may be getting smaller with each step of the way2) enough sample size, VC bound can ensure that Ein and eout close (good generalization)Lin then introduces a classic example of a adaboost:To find a weak classifier, that is no weaker than the one-dimension stump, but it is so weak classifier, through

-validation set and the test set will increase according to the ratio of 6 2 2.1) When using a relatively suitable model, when the data is relatively small, jtrain will be perfect fitting training data, but at this time JCV will be relatively large, because the data of the model is very difficult to fan the cross-validation set, the increase in

factorzation is a more common one is the stochastic Gradient descent method.In the optimization of the Ein, regardless of the preceding constants, consider the following equation.Because there are two variables, the gradient needs to be calculated separately. Can consult the SGD algorithm, here is the simplest derivative, no longer repeat.Here's a more: Why is the derivation of Vn only considered (RNM-WM ' vn) ² this item?Because, here the derivative has two variables, vn and WM:1) Items that d

written in front: These one months are learning python, from the Python3 Foundation, Python crawlers, Python data mining and data analysis have contact, recently saw a machine learning book (mainly

the integrated tree model, the feature selection factor and sample usage factor of each tree. In the project, considering the accuracy and speed, the final parameter is that the number of trees is 20, both the feature selection factor and sample selection factor are 0.65 (0.65 of samples and features are randomly selected for training on each tree) For specific product results, see the sorting results of the Baidu keyword search Recommendation System in www2.baidu.com:How to personalize The fir

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 Stat

This section is about the nuclear svm,andrew Ng's handout, which is also well-spoken.The first is kernel trick, which uses nuclear techniques to simplify the calculation of low-dimensional features by mapping high-dimensional features. The handout also speaks of the determination of the kernel function, that is, what function K can use kernel trick.In addition, the kernel function can measure the similarity of two features, the greater the value, the

, each sample D dimension characteristics, in order to measure the importance of the I-dimensional features, can be the nth sample of the I-dimensional features are shuffle upset. Re-evaluation of the pre-shuffle and shuffle after the model performance.However, there is a problem, must constantly shuffle, training, the process is very cumbersome.So the RF author thought of a somewhat lazy trick, as follows:Training, do not play permutation, change in validation time play permutation: that is, th

Python; It was sufficient for it to have a Python interface. We also have a small sections on deep learning at the end as it has received a fair amount of attention recently. We do not aim for list all the machine learning libraries available in Python (the Python PAC Kage Index returns 139 results for ' machine

require the library to being written in Python; It was sufficient for it to have a Python interface. We also have a small sections on deep learning at the end as it has received a fair amount of attention recently. We don't aim to list all the machine learning libraries available in Python (the Python package index returns 139 results for "

This is what we have learned (except decision tree)Here is a typical decision tree algorithm, with four places to choose from:Then introduced a cart algorithm: By decision Stump divided into two categories, the criterion for measuring subtree is that the data are divided into two categories, the purity of these two types of data (purifying).The following is a measure of purity:Finally, when to stop:Decision

instrumental permutation test (permutation test) in the use of statistics in RF is used to measure the importance of feature items. n samples, D dimensions per sample, in order to measure the importance of one of the features di, according to permutation test the N sample of the di features are shuffled shuffle, shuffle before and after the error subtraction is the importance of this feature. RF often does not use permutation Test during training, but instead disrupts the OOB feature it

to Gini If the node cannot be divided, save the node as a leaf node Execute Two-dollar segmentation In the right subtree recursive call Createtree () method, create subtree In the right subtree recursive call Createtree () method, create subtree Four, cart and AdaBoost meta-algorithm application comparison Cart is more efficient than adaboost because the former is "conditionally cut" and the latter is completely "horizontal and vertical". Five, the characteristics of the

a female, it is 0. Therefore, we can write the current regression equation as follows: weight = a + b*height + c*isManHere we only use one of the isman methods of the sex method. Suppose there are N values for the Virtual Variables (male and female here ), then, only n-1 values (isman) can be written in the regression equation ). For the above regression equations, we can obtain the values of A, B, and C respectively, but the values of isman are 0 or 1, so the values of C * isman are C or 0

Course Address: Https://class.coursera.org/ntumltwo-002/lectureImportant! IMPORTANT-Important ~First, the motive of Adaptive boostingBy combining multiple weak classifiers (Hypothese), a more powerful classifier (Hypothese) is built to achieve the effect of "Three Stooges equals".In practice, for example, a more complex model can be composed of simple "horizontal" "vertical".Second, the sample weightA very important concept in the AdaBoost meta-algorithm is called sample weight U.Learning algori