Multi Class

We used the 2004 Reuters news dataset. The training set has about 10,000 news examples, and the test set is 50% of this data. The original dataset has 103 categories that are organized into four hierarchies: ![enter image description here][1] - Corporate-Industrial (CCAT) - Government and Social (GCAT) - Economics and Economic Indicators (ECAT) - Securities and Commodities Trading and Market (MCAT) For this experiment, we used the names of the hierarchies as the label, or attribute to predict. Thus we were solving a multiclass classification problem with four classes. The original news articles might belong to one or more hierarchies. For those articles, a separate example was created for each combination of label and article, so that the articles had the same features but different label. For instance, if an article belonged to CCAT and GCAT, two examples would exist in the label data set, one for CCAT, and the other one for GCAT. ![enter image description here][2] <br><br> ---------- > This ML experiment is for [Microsoft Azure Machine Learning Course][101].<br> For the complete experiment list [Click here][102].<br> Laploy | laploy@gmail.com | 084 007 5544 | [www.laploy.com][103]<br> ![enter image description here][104] ---------- [101]: https://notebooks.azure.com/laploy/libraries/loyml/html/00001%20Sessions%20summary.ipynb [102]: https://gallery.cortanaintelligence.com/Home/Author?authorId=81E333F747E3429B55A3445E6714C36F60B397C13B4D0B07F34DEF1421F64D73 [103]: http://laploy.com [104]: https://raw.githubusercontent.com/laploy/mli/master//loy-small.jpg [1]: https://raw.githubusercontent.com/laploy/mli/master//12530-000.jpg [2]: https://raw.githubusercontent.com/laploy/mli/master//12530-001.jpg [3]: https://gallery.cortanaintelligence.com/browse?s=laploy