It's inevitable to create a training set with custom labels to handle specific needs for a unique business process.
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Building a machine learning model process starts with a well-prepared and well-balanced training set. Kimola accepts training sets in .xlsx, .xls, .csv and .tsv file formats. Just drag & drop or upload your training set file and let Kimola analyse whether it’s suitable or not to build as a machine learning model.
Once you create a model you will have options to classify a single text block or an entire dataset file with hundreds of thousands of rows automatically. Kimola also encourages developers to integrate their applications with Kimola to benefit from Artificial Intelligence without any infrastructure investment.
Kimola offers AutoML technology designed to build a machine learning model with the highest accuracy rate possible. This allows social scientists to focus on the content of the training set rather than the technical implementations.
When you modify your existing training set, Kimola rebuilds the model using the same AutoML pipeline. This way, you can keep your training set up-to-date and ensure it's built with the most suitable statistical method.
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Since data analytics is a highly sophisticated area, besides offering a good UI/UX, Kimola provides educational content such as articles and video tutorials. Also, you will have live chat support in the dashboard.
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Yes, just focus on creating a well-prepared training set. It may be in Excel format or in case you are not a fan of Excel, use your favorite software and export as CSV or TSV. Kimola accepts all of these file extensions and build your machine learning models. Don't worry, if tehere anything wrong with your training set, the wizard will notify you.
You can upload a training set with minimum of two labels and 100 rows of sample for each label. A training set can contain maximum of 60.000 rows and 15 labels at maximum.
No, Kimola is only available on the cloud. Keep in mind that you can perform all the tasks related tou your custom model trough a visual user interface or a well-documented API. If you have any concerns about your data safety just contact us we can provide all the information you need.
Kimola's AutoML will try its best to hit the best accuracy rate but in the end it's all about the training set. So, if you get a low accuracy rate simply add more samples for your labels and upload your training set again. It does not effect your package usage because we know that these experiments is the natural part of the process.
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