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Decision Tree vs Neural Network Short-Term Regression Models Part 3 of 3.

Writer's picture: Tian Khean NgTian Khean Ng

Data as of 10 Jan 25

In the final part of our post on this topic we explain why Neural Networks perform poorly on short-term Price data.

There are several possible explanations for why Decision Tree models outperform  Neural Networks.  Here are some key factors to consider:

1. Data Size and Complexity:

  • Small Dataset: With only 250 data points, the dataset is relatively small for training a Neural Network. Neural Networks typically require large amounts of data to perform well, as they have many parameters to learn. In contrast, Decision Trees can often perform well with smaller datasets and are less prone to overfitting when the trees are not too deep.

  • Model Complexity: Neural Networks are highly flexible and can model complex, non-linear relationships. However, this flexibility comes at the cost of requiring more data to train effectively. If the relationships in your data are not sufficiently complex, a simpler model like a Decision Tree might generalize better.

2. Feature Space:

  • Feature Engineering: Decision Trees can work well with raw data and do not require extensive feature engineering. They can automatically select the most important features and split the data based on those features. Neural Networks, on the other hand, often benefit from feature engineering, such as normalization, scaling, and the creation of new features. If the features are not properly engineered, the Neural Network might not perform as well.

  • Dimensionality: With only three input features (Open, High, Low), a Decision Tree can easily handle the feature space, while a Neural Network might struggle to find meaningful patterns in such a low-dimensional space, especially with a small dataset.

3. Model Interpretability and Overfitting:

  • Overfitting: Neural Networks are prone to overfitting, especially when the dataset is small. Overfitting occurs when the model learns the noise in the training data, which can lead to poor generalization on unseen data. Decision Trees can also overfit, but techniques like pruning can help mitigate this. If the Neural Network is overfitting, it could perform worse on the validation or test set.

  • Regularization: Neural Networks require careful regularization (e.g., dropout, L1/L2 regularization) to prevent overfitting. If these techniques are not applied correctly, the model might overfit the training data.

4. Model Architecture and Hyperparameters:

  • Architecture Design: Neural Networks require careful design of the architecture (e.g., number of layers, number of neurons per layer, activation functions). If the architecture is not well-suited to the problem, the model might not perform well. Decision Trees, on the other hand, are generally robust and easier to tune and require fewer hyperparameters.

  • Hyperparameter Tuning: Neural Networks have many hyperparameters (e.g., learning rate, batch size, number of epochs) that need to be tuned. If the hyperparameters are not optimized, the model might not perform well. Decision Trees also have hyperparameters (e.g., maximum depth, minimum samples per leaf), but they are often easier to tune and require less experimentation.

5. Nature of the Data:

  • Non-Linear Relationships: If the relationships between the input features (Open, High, Low) and the output (Close) are non-linear and complex, Neural Networks might still perform better. However, if the relationships are relatively simple or linear, a Decision Tree might capture them just as well or even better.

  • Noisy Data: Stock market data is often noisy and influenced by many external factors. Decision Trees can be robust to noise in certain situations, especially if the tree is not too deep. Neural Networks, especially with insufficient data, might struggle to generalize in the presence of noise.

6. Interpretability and Simplicity:

  • Simplicity: Decision Trees are often easier to interpret and can provide insights into the decision-making process. This simplicity might lead to better performance in cases where the relationships are not too complex.

  • Bias-Variance Trade-off: Decision Trees might have a higher bias# but lower variance, which can be beneficial when the dataset is small. Neural Networks, with their high capacity, might have lower bias but higher variance, leading to overfitting. #In Regression Bias can be visualized as the Intercept of a multi-dimensional. For example, in a 2-dimensional model where X is the horizontal axis and Y the vertical axis, the Bias value is the point at which the data begins at the Y axis. If there is no bias, the data starts at both X=0 and Y=0.

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