By George A Duckett
When you've got a question approximately facts technological know-how this can be the booklet with the solutions. info Science: Questions and solutions takes the very best questions and solutions requested at the datascience.stackexchange.com web site. you should use this booklet to seem up frequently asked questions, browse questions about a specific subject, evaluate solutions to universal themes, try out the unique resource and masses extra. This e-book has been designed to be really easy to take advantage of, with many inner references arrange that makes searching in lots of other ways attainable. themes lined contain: laptop studying, Bigdata, information Mining, category, Neuralnetwork, statistics, Python, Clustering, R, textual content Mining, NLP, Dataset, potency, Algorithms, Hadoop, SVM, instruments, suggestion, Visualization, Databases, function choice, NoSQL, ok capability, Random woodland, Logistic Regression and lots of extra.
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3. Well, ultimately it depends on your dataset, and the different models you have tried. At this point, and without further testing, there can not be a definite answer. 4. Without claiming to be an expert on the topic, there are a number of different techniques you may follow (hint: first link on google ), but in my opinion you should first make sure you choose your cost function carefully, so that it represents what you are actually looking for. 5. Not sure what you mean by pattern intuition, can you elaborate?
Either vanilla lexical distance metrics or state-of-the-art semantic distance metrics are preferred, with stronger preference for the latter. movies, music, commercial queries and so on). See “Introduction to Information Retrieval” book for details. I would also recommend doing LSA first on tf idf weights, and then computing the cosine distance\similarities. If you are trying to build a search engine, I would recommend using a free open source search engine like solr or elastic search, or just the raw lucene libraries, as they do most of the work for you, and have good built in methods for handling the query to document similarity problem.
A strong linear correlation between the new feature and the predicted variable is an good sign that a new feature will be valuable, but the absence of a high correlation is not necessary a sign of a poor feature, because neural networks are not restricted to linear combinations of variables. Whenever possible, prefer learning features to engineering them. Tags: machine-learning (Prev Q) (Next Q), neuralnetwork (Prev Q) (Next Q), featureselection (Prev Q) (Next Q) Q: How to increase accuracy of classifiers?