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Correlation exposes the relationship between variables. We can use correlation to understand whether changing the values for variable A is likely to also change the values for variable B. This article explains how correlation works, the different types of correlation alongside pointing out how to measure degrees of correlation in Graphext.
'España' and 'Españha' are just spelling variations. We built a way of grouping words spelt differently but referring to the same concept.
After you build a prediction model, you'll find the Models panel in your project letting you inspect how your model was built and how it performed. This article helps you interpret the information inside the three sections of your Models panel.
UMAP and our proprietary k-NN graph treat the relative balance between categorical and numeric data differently, leading most of the time to qualitatively different embeddings.
One of Graphext’s most differentiating features is the way we allow you to see all your data at a glance in a single “topological map”, or Graph. But how does it work?
Some steps allow the use of advanced filter queries to sample specific rows in a dataset. An advanced query allows you to filter data using a simple text query, the syntax of which may be familiar to users of SQL or Elasticsearch.
You can train and deploy prediction models in Graphext. Models use existing features of your data to predict the value of a target variable.
Somewhere, in a parallel universe ... there is only one format for data files. Here on Earth, Graphext supports a range of different file types.
Working with text data involves analyzing text in many languages. We support many languages for text analysis including English, Spanish, French, German, Italian, Portuguese, Arabic, Turkish, Basque and Catalan.
