I am teaching myself machine learning through the book "Introduction to Machine Learning with Python: A Guide for Data Scientists", and I am currently at the k-Nearest Neighbors section. The authors mention that this algorithm is rarely used in real life due to "prediction being slow and its inability to handle many features". However, the k-Nearest Neighbors is mentioned as one of the most popular algorithms for data scientist in many articles. So, could somebody explain it for me here?
K-nearest neighbor has a lot of application in machine learning because of the nature of the problem which is solved by a k-nearest neighbor. In other words, the problem of the k-nearest neighbor is fundamental and it is used in a lot of solutions. For example, in data representation such as tSNE, to run the algorithm we need to compute the k-nearest neighbor of each point base on the predefined perplexity.
Also, you can find more application of kNN here and its application in the industry in the last page of this article.
The KNN algorithm is one of the most popular
algorithms for text categorization or text mining.
Another interesting application is the evaluation of forest
inventories and for estimating forest variables. In
these applications, satellite imagery is used, with the
aim of mapping the land cover and land use with few
discrete classes. The other applications of the k-NN
method in agriculture include climate forecasting and
estimating soil water parameters.
Some of the other applications of KNN in finance are
mentioned below:
Forecasting stock market: Predict the price of a
stock, on the basis of company performance
measures and economic data.
Currency exchange rate
Bank bankruptcies
Understanding and managing financial risk
Trading futures
Credit rating
Loan management
Bank customer profiling
Money laundering analyses
Medicine
Predict whether a patient, hospitalized due to a
heart attack, will have a second heart attack. The
prediction is to be based on demographic, diet
and clinical measurements for that patient.
Estimate the amount of glucose in the blood of a
diabetic person, from the infrared absorption
spectrum of that person’s blood.
Identify the risk factors for prostate cancer,
based on clinical and demographic variables.
The KNN algorithm has been also applied
for analyzing micro-array gene expression data,
where the KNN algorithm has been coupled with
genetic algorithms, which are used as a search tool.
Other applications include the prediction of solvent
accessibility in protein molecules, the detection of
intrusions in computer systems, and the management
of databases of moving objects such as computer
with wireless connections.
I started learning when and where the algorithms for machine learning applied. For anomaly detection algorithms, if you look at Azure machine learning algorithm cheat sheet, it says, “100 features”, so does the same for Two class SVC under classification. What does 100 feature mean in both of the algorithms?
Please look more closely: it's not "100 feature", it is simply an indication of " > 100 features", i.e that you can use more than 100 features in the respective algorithm (One-class SVM)... – desertnaut
I inspect the following content that I want to extract.
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Under the direction of the supervisor, this position is responsible for providing data synthesis and analysis for the Northern Great Plains (NGP). S/he will assist the NGP program in communicating its goals and successes through the development of data synthesis products and developing statistical interpretation of existing and new datasets, with a focus on informing grassland conservation. S/he will develop products to disseminate information to NGP staff and partners. \n \n Responsibilities Provide data synthesis and interpretation for existing and new datasets to support grassland conservation goals. Work with existing datasets to develop new ways of interpreting the data and communicating it to partners. Work with new datasets to help answer key science questions as outlined by the Program Manager. Given the list of science priorities for the program, develop methods for answering pressing questions using the best available data. Develop spatial data for use in projects. Collect and process datasets for use by NGP staff and partners, as needed and in partnership with the GIS Specialist. Support NGP Program by developing in-depth knowledge of grassland conservation and researching and developing skills in other approaches necessary to ensure success of WWF’s conservation strategies in the region. Build knowledge through research to keep up to date with the state of the art knowledge and apply the knowledge to WWF projects. The candidate will report to the Program Manager. S/he will also maintain strong relationships with the Managing Director, Deputy Director, NGO partner organizations, federal, state and provincial agency planning personnel and corporate and foundations staff at WWF-US. \n Qualifications A Master of Science Degree in Biostatistics, Biology, Conservation Biology, Zoology, Ecology, Wildlife Management, or a related field, is required 4+ years of experience in spatial analysis and data synthesis is required. A PhD will substitute for 3 years of work experience. Substantial and demonstrated experience in spatial analysis; data synthesis; and managing an independent work program is required Experience in biodiversity conservation and grassland-focused spatial datasets is preferred Candidates should have a strong commitment to the mission, goals, and values of WWF, good interpersonal and relationship-building skills, energy and enthusiasm, and high ethical standards. \n Please Note: This is a 2-year position based in Bozeman, Montana. \n To Apply: Please visit our Careers Page, job#17065, to submit an online application including resume and cover letter Due to the high volume of applications we are not able to respond to inquiries via phone As an EOE/AA employer, WWF will not discriminate in its employment practices due to an applicant’s race, color, religion, sex, national origin, and veteran or disability status."},"applyMethod":{"com.linkedin.voyager.jobs.OffsiteApply":{"applyStartersPreferenceVoid":true,"companyApplyUrl":"https://careers-wwfus.icims.com/jobs/1727/data-analyst---17065/job"}},"title":"Data Analyst","listedAt":1496950791000}
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I tried several different ways to extract the content, especially the longest text part, such as
body.xpath('//code[#id="bpr-guid-1441788"]/text()').extract()
But there is no response, the return of scrapy is null.
Anyone can help me out?
I'm trying to learn more about trust metrics (including related algorithms) and how user voting, ranking and rating systems can be wired to stiffle abuse. I've read abstract articles and papers describing trust metrics but haven't seen any actual implementations. My goal is to create a system that allows users to vote on other users and the content of other users and with those votes and related meta-data, determine if those votes can be applied to a users level or popularity.
Have you used or seen some sort of trust system within a social graph? How did it work and what were its areas of strength and weaknesses?
I'm reading the book Programming Collective Intelligence.
From the description:
Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet.
The algorithms in the book are implemented in python.
I've just started reading the book so I don't know if it can help solve your problem, but it's worth taking a look.