Discussing the article: "Integrating ML models with the Strategy Tester (Conclusion): Implementing a regression model for price prediction"
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Check out the new article: Integrating ML models with the Strategy Tester (Conclusion): Implementing a regression model for price prediction.
This article describes the implementation of a regression model based on a decision tree. The model should predict prices of financial assets. We have already prepared the data, trained and evaluated the model, as well as adjusted and optimized it. However, it is important to note that this model is intended for study purposes only and should not be used in real trading.
Based on the above criteria, in this article, I decided to use the Decision Tree Regression model to predict the closing price. The choice of this model is justified for the following reasons:
Performance: Decision trees typically work well for regression problems because they are able to capture nonlinear relationships and interactions between variables. By properly tuning model hyperparameters, such as tree depth and minimum number of samples per leaf, we can achieve a balance between fitness and generalization.
Interpretability: One of the main advantages of decision trees is their interpretability. Decision trees are a series of decisions based on attributes and their values, making them easy to understand. This is useful for justifying forecasts and understanding the factors influencing closing prices.
Complexity: The complexity of decision trees can be controlled by tuning the hyperparameters of the model. With this, we canfind a balance between the ability to model complex relationships and the simplicity of the model, while avoiding overfitting.
Training time: Decision trees typically train relatively quickly compared to more complex models such as neural networks or SVMs. This fact makes the decision tree regression model suitable for cases where training time is an important factor.
Robustness: Decision trees are robust to outliers and noise in the data because each decision is based on a set of samples rather than a single observation, and this contributes to the stability of predictions and the reliability of the model.
Author: Jonathan Pereira