By George A Duckett
When you've got a query approximately info technological know-how this is often the ebook with the solutions. info Science: Questions and solutions takes the superior questions and solutions requested at the datascience.stackexchange.com site. you should use this booklet to seem up frequently asked questions, browse questions about a specific subject, examine solutions to universal themes, try out the unique resource and masses extra. This ebook has been designed to be really easy to exploit, with many inner references manage that makes shopping in lots of other ways attainable. subject matters lined contain: laptop studying, Bigdata, facts Mining, category, Neuralnetwork, facts, Python, Clustering, R, textual content Mining, NLP, Dataset, potency, Algorithms, Hadoop, SVM, instruments, advice, Visualization, Databases, characteristic choice, NoSQL, ok ability, Random woodland, Logistic Regression and lots of extra.
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Additional info for Data Science: Questions and Answers
Not sure what you mean by pattern intuition, can you elaborate? Were they any different? In this field, new frauds appear regularly, so that new features have to be added to the model on ongoing basis. I wonder what is the best way to handle it (from the development process perspective)? Just adding a new feature into the feature vector and re-training the classifier seems to be a naive approach, because too much time will be spent for re-learning of the old features. How can I choose an algorithm for the overall classifier?
This is not an issue for generalizability, but it bears heavily on your considerations for sample size. For this reason, blacks and Latinos were deliberately oversampled. These can be used to re-weight the sample so as to reflect the estimated population proportions, in the event that a representative sample is required. This and some other sampling designs are reviewed in surprising depth on Wikipedia . This is a lecture given by Andrew Ng about them.
E. something that determines sampling parameters via wrapper or a modification of a bagging framework that samples to class equivalence), then I would suggest again feeding the representative sample and letting the algorithm take care of balancing the data for training. But in many cases the costs of missing postive examples is high so you have to find a solution for it. For example in the case of medical diagnosis data analysis. In summary: Classification erros do not have the same cost! Answer by damienfrancois There always is the solution to try both approaches and keep the one that maximizes the expected performances.
Data Science: Questions and Answers by George A Duckett