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In the realm of machine learning, more often than not we think in terms of trade offs. While optimising one metric of performance, we often compromise another performance metric. With the growing evolutionary trend of increasingly larger and more intricate models, understanding, explaining and debugging them become formidable tasks. The intricacies beneath the model's surface, deciphering 'why' our models are making the decisions they are making is vital. Without this clarity how can we confidently

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Regression is a task of predicting a real value from an unlabeled example. A well-known example of regression is estimating the value of a diamond based on such characteristics as size, weight, color, clarity, etc.

The so-called regression metrics are used to assess the accuracy of regression model predictions. Despite similar algorithms, regression metrics are semantically different from similar loss functions.

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This algorithm is based on a self-learning method, where the agent uses information obtained during interaction with the environment to generate "intrinsic" rewards and update its strategy. The algorithm is based on the use of several agent models that interact with the environment and generate various predictions. If the models disagree, it is considered an "interesting" event and the agent is incentivized to explore that space of the environment. In this way, the algorithm incentivizes the agent

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Behavior cloning methods, largely based on the principles of supervised learning, show fairly good results. But their main problem remains the search for ideal role models, which are sometimes very difficult to collect. In turn, reinforcement learning methods are able to work with non-optimal raw data. At the same time, they can find suboptimal policies to achieve the goal. However, when searching for an optimal policy, we often encounter an optimization problem that is more relevant in high-dimensional

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These series of articles, on the MQL5 Wizard, are a segue on how often abstract ideas in Mathematics of other fields of life can be enlivened as trading systems and tested or validated before any serious commitments is made on their premise. This ability to take simple and not fully implemented or envisaged ideas and explore their potential as trading systems is one of the gems presented by the MQL5 wizard assembly for expert advisers. The expert classes of the wizard furnish a lot of the mundane

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