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It is important to have a rich and comprehensive data set for every system decision, including machine learning models. Only then can one build a robust and powerful model with desired performance. Otherwise, the developer would face the so-called GIGO (garbage in – garbage out) effect. It is an expression that says poor-quality input data provides poor-quality outputs. Although there are several data platforms automating the whole ETL (extract, transform, load) process, sometimes these solutions are overabundant, or the pre-made solutions might not meet your specific needs. (more…)
The use of machine learning in the quantitative investment field is expanding and the right usage is encouraging many to explore the world of investing. To generate market advantages and enable our users to enjoy the greatest stock selection process, we are choosing beneficial data, suitable to give us not only the Artificial-Intelligence-driven tool for making the right stock decisions but also interpretability and transparency of process and results. Our calculations include data collected for the last 30 years, allowing us to get more robust and reliable results.
Reviewing learning curves of models during training and plots of the measured performance can be used to diagnose problems with learning, such as an underfitting or overfitting model. Besides that, it can also be used for diagnosing whether the training and validation datasets are suitably representative and to observe generalization behavior, as well. Our team uses the learning curve visualisation to built more robust, reliable and interpretable Stock Picking Lab model.
As part of the research used, for example, in the StockPicker application, we have recently implemented the method Feature Binning and Quantile Transformation to better classify data. Due to upgraded data preparation, our machine learning models now achieve better results.
- You can see this in detail in the following lines.
- If you need to extract information from your data, do not hesitate to contact us.
