big eraser for big mistakes

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Several big mistakes in data mining are "reproduced, invaded and deleted"

times times the weight of those defaulting clients). Modeling shows that as the model becomes more and more complex, the accuracy of discriminating clients is more and more high, but the rate of miscarriage of normal customers increases. (The problem is the partitioning of the data set.) When the original data set is divided into training and test sets, the weights of default customers in the original dataset have been increased.Workaround: First the data set division, and then improve the trai

10 big mistakes in agile development

Ten big mistakes in agile developmentOriginal: Http:// MeaseyTranslator: Zhang Sb er For the rapid development of agile software development field, this article will be the most common error understanding of the analysis.In today's global market context, how to be flexible, for an

10 big mistakes in agile development

10 big mistakes in agile development Original: Http:// Author: Peter Measey Translator: Zhang Sb er Absrtact: For the rapid development of agile software development field, this article will be the most common error understanding of the analysis. In today's global market context, how to be

Can you get some coffee for your big aunt? Five mistakes in menstrual period

medical attention in timeExpert introduction: "Dysmenorrhea is divided into pathological dysmenorrhea and physiological dysmenorrhea." Female menstruation mainly by the hypothalamus-pituitary-ovary reproductive axis regulation, menstruation normal or not with the female physiological state is closely related, but unfortunately many girls because do not know the menstrual pain behind may hide the disease that affects fertility, always put dysmenorrhea improper return to matter. Every dysmenorrhe

Small mistakes cause big failures

that such a simple activity would actually fail.I think it is very important to carry out the actual activities and write code is very different, in the virtual world, there are many things can be replaced, or circuitous implementation, but the reality of a lot of things are irreplaceable.Non-substitution causes all nodes to be concatenated, and the entire system will not function as long as any of the nodes are faulty.OK, about the front-end interest group such activities, in fact, a little an

6 Big mistakes that web designers should avoid

clear and orderly. If there are too many similar colors, the distinction becomes difficult, on the other hand, strong color conflicts will appear messy and unattractive.To ensure better readability and overall usability, colors should not go out of bounds. There should be a color harmony between each other, so that the site will not appear abrupt and unsightly. Whether it is web design or classic art design, attention to color balance has a great impact on the overall appearance. For starters,

C + + Primer learning notes and thinking _10 type conversion easy mistakes big Summary

conversion is performed between class hierarchies;In the downstream conversion, dynamic_cast has the function of type checking, which is more secure than static_cast.Also note: A should have virtual function, otherwise it will compile error; static_cast does not have this limitation.This isbecause run-time type checking requires run-time type information, this information is stored in the class's virtual function table(With regard to the concept of virtual function tables, in detail #include (i

Google's recent three big big domestic website optimization needs to find the right direction

website This even search engine is not very good judgment, and this is not the site's own mistakes, so generally found that black chain after the black chain of the site should not be too big, and hanging black chain of the site may be more miserable, directly will be down right. Remedial direction: The strength is stronger, the weight is relatively high website, the proposal does not carry on the link tr

The application of factor space theory in big Data--Wang Peizhuang

data is only the quicksand that is analyzed, now it becomes our well-cultivated object. We want to keep the overlay of the sample, and when it represents the matrix, all the reasoning knowledge is generated by it. We are not afraid of it making mistakes, where they make mistakes, and where they stack up, it is a step closer to the real mother. A mature sample, that is, no longer or rarely make

iSEE let mm eyes suddenly become big among non-mainstream

satisfactory, you can choose "Eraser" to erase, and then use the "Zoom pen" processing. Operation Tip: 1. The magnifying pen is best to approach and slightly larger than the size of the eye. 2. Will enlarge the intensity slightly to adjust the big point, achieves the mouse to click once to complete the eye to become big. In this way, the eye be

When traditional companies meet big data

analysis process. In the process of building a sharing platform, there are several points to note:L Personnel attention mechanism . Because the enterprise operation is different from the Internet, there are certain closed characteristics, not recommended the use of Weibo attention to openness, but should use a friend circle similar "concern-consent" of the friend mechanism, to avoid inappropriate attention to bring about the disclosure of information;L share range control . Because of the parti

Use Python for big data analysis

one meets your requirements. We recommend that you use IPython Notebook, Rodeo, and Spyder at the beginning. Like a variety of Ides, Python also provides a variety of data visualization libraries, such as Pygal, Bokeh, and Seaborn. Matplotlib is the most essential tool for data visualization. it is a simple and effective numerical drawing class library. All these databases are included in Anaconda, so after downloading them, you can study which tool combinations can better meet your needs. You

Big endian and little endian

, both arm and DSP are in the small-end mode by default, but many of them can change the size of the end mode by setting. At present, many complier are small-end models, but you writeCodeBoth modes should be supported at the same time, and macro should be used for switching. This test should be added to your test plan. This is necessary and will be analyzed below. How to test the core or complier size-end mode. There is a simple way to add the following code (Google, some are simpler) Short

iSEE let mm eyes suddenly become big

First look at the original picture and effect chart. Original: (Figure 1) Big Eye Effect Chart: (Figure 2) 1.iSee Open Figure 1. Select "Right sidebar"-Portrait beauty-eyes become larger. (Figure 3) 2. Into the "eyes become larger" processing interface, select "Enlarge Pen". Pen Size: 37 (The magnifying pen is best to approach and slightly larger than the size of the eye) Amplification Strength: 30 (can be

Using Python for Big data analysis

basics of Python, you need to know how it works and what you need for the data Science library. The key points include NumPy, a base class library that provides advanced mathematical computing capabilities, SciPy, a reliable class library focused on tools and algorithms, Sci-kit-learn for machine learning, and pandas, a set of tools to provide operational dataframe functionality. In addition to the class library, it is also necessary to know that Python is not recognized as the best integrated

Turn: a classic dialogue similar to the big wrist

A classic dialogue similar to the big wrist: Find the most popular framework,Use the most powerful editor,The most complex system is required,Lightweight,The simplest framework is spring,What EJB, hibernate, seam, and all that can be used,The presentation layer should be configurable and the persistence layer should be replaceable,ProgramIt would be better to use 10 thousand years,When the customer sees each other, render manager is okay,You have

Summary of the post-reading of the Big Data era

, and I think the third shift is the top priority of the book. Big data tells us "what" rather than "why", in the big data age, we sometimes don't need to know the reason behind the phenomenon, we just have to let the data speak for themselves and the relationship will shine.The relationship can predict the occurrence of events, the trend of the situation, in short, to predict the future, the book mentions

Is the architect a big bluff? Ali Technology Daniel tells you the truth!

is the architect a big bluff? Ali Technology Daniel tells you the truth. Source: Aliyun Author: Lin Hao went (nickname Bi Xuan), Alibaba technical support researcher, has been a Taobao platform architecture Division architect. The personal research direction mainly is the Java modular, the dynamic system construction, as well as the high-performance large-scale distributed Java system construction, leads Ali data Center off-site many live project

Black Swan and Big Data

. Even the choice of ordinary people in daily life also exists many black swan phenomena. I wrote a sentence on Weibo: I have two friends who are tough and don't give up on the company, over more than a decade, I have grown from a programmer to a vice president of technology, and the company has collapsed due to a sudden financial scandal ...... Another friend is always in the middle of his job-hopping. After two years of work in a company, the company went public, and he went to NASDAQ for a mi

Quantification: Enterprise Management in the big data age

Quantification: Enterprise Management in the big data ageBasic InformationOriginal Title: metrics: how to improve key business resultsAuthor: (US) Martin klubeck [Translator's introduction]Translator: Wu HaixingPress: People's post and telecommunications PressISBN: 9787115299611Mounting time:Published on: February 1, January 2013Start: 16Page number: 1Version: 1-1Category: Computer> database storage and management More about "Quantification: Enterpris

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