What does it really mean? Math books and even my beloved Wikipedia describe e using obtuse jargon: The mathematical constant e is the base of the natural logarithm.
Weka algorithm A series of repeatable steps for carrying out a certain type of task with data. As with data structures, people studying computer science learn about different algorithms and their suitability for various tasks.
Specific data structures often play a role in how certain algorithms get implemented. AngularJS is popular with data scientists as a way to show the results of their analysis.
As the cost of computing resources dropped, the focus moved more toward statistical analysis of large amounts of data to drive decision making that gives the appearance of intelligence.
See also machine learningdata mining backpropagation Also, backprop. An algorithm for iteratively adjusting the weights used in a neural network system.
Backpropagation is often used to implement gradient descent.
An equation for calculating the probability that something is true if something potentially related to it is true. The theorem also makes it easier to update a probability based on new data, which makes it valuable in the many applications where data continues to accumulate.
Named for eighteenth-century English statistician and Presbyterian minister Thomas Bayes. See also Bayesian networkprior distribution Bayesian network Also, Bayes net. These graphs aid in performing reasoning or decision making in the face of uncertainty.
Variance is the tendency to learn random things irrespective of the real signal Simultaneously avoiding both requires learning a perfect classifier, and short of knowing it in advance there is no single technique that will always do best no free lunch.
A key driver of this new ability has been easier distribution of storage and processing across networks of inexpensive commodity hardware using technology such as Hadoop instead of requiring larger, more powerful individual computers.
The work done with these large amounts of data often draws on data science skills. This is a discrete probability distribution, as opposed to continuous—for example, instead of graphing it with a line, you would use a histogram, because the potential outcomes are a discrete set of values.
As the number of trials represented by a binomial distribution goes up, if the probability of success remains constant, the histogram bars will get thinner, and it will look more and more like a graph of normal distribution.
See also normal distribution and Wikipedia on the chi-squared test and on chi-squared distribution. Deciding whether an email message is spam or not classifies it among two categories, and analysis of data about movies might lead to classification of them among several genres.
See also supervised learningclustering clustering Any unsupervised algorithm for dividing up data instances into groups—not a predetermined set of groups, which would make this classification, but groups identified by the execution of the algorithm because of similarities that it found among the instances.
See also correlation computational linguistics Also, natural language processing, NLP. A branch of computer science for parsing text of spoken languages for example, English or Mandarin to convert it to structured data that you can use to drive program logic.
Early efforts focused on translating one language to another or accepting complete sentences as queries to databases; modern efforts often analyze documents and other data for example, tweets to extract potentially valuable information.If you are an R blogger yourself you are invited to add your own R content feed to this site (Non-English R bloggers should add themselves- here).
e is NOT Just a Number. Describing e as “a constant approximately ” is like calling pi “an irrational number, approximately equal to ”.
Sure, it’s true, but you completely missed the point. e is NOT Just a Number. Describing e as “a constant approximately ” is like calling pi “an irrational number, approximately equal to ”.
Sure, it’s true, but you completely missed the point. CHAPTER 4 Exponential and Logarithmic Functions Properties of Logarithms OBJECTIVES 1 Work with the Properties of Logarithms 2 Write a Logarithmic Expression as a Sum or Difference of Logarithms 3 Write a Logarithmic Expression as a Single Logarithm 4 Evaluate Logarithms Whose Base Is Neither 10 nor e 5 Graph Logarithmic Functions Whose Base Is Neither 10 nor e.
In statistics, a confidence interval (CI) is a type of interval estimate, computed from the statistics of the observed data, that might contain the true value of an unknown population leslutinsduphoenix.com interval has an associated confidence level that, loosely speaking, quantifies the level of confidence that the parameter lies in the interval.
More strictly . If you are an R blogger yourself you are invited to add your own R content feed to this site (Non-English R bloggers should add themselves- here).