Why You Can't Skip Data Preprocessing in Machine Learning
In the fast-paced industry of artificial intelligence, machine learning has become a go-to solution for a variety of complex problems, from predicting how consumers will behave to diagnosing diseases. However, before machine learning algorithms can start operating, they need to go through a key process: data preprocessing.
To ensure your model is generating accurate, reliable, and meaningful results, data preprocessing is more than a requirement: it is the essence of the solution. This is especially true when developing machine learning systems in finance, where quality preprocessing is the difference between success and failure.
What is Data Preprocessing?
Data preprocessing refers to the process of converting raw data into a clean and structured format for machine learning algorithms to appropriately understand and process. Real-world data is typically not ready for analysis; it is often insufficient, inconsistent, or noisy. This means that preprocessing will improve the quality of the analysis by including data cleaning, data normalization, data transformation, and feature selection.
In finance, the signal-to-noise ratio, a metric specifying how variable time series are, is very low. Several studies suggest potential techniques to avoid fitting models to noise, and while there is no one-size-fits-all solution, different methods are available to data scientists in order to improve the performance of algorithms and models. The best approach heavily depends on the application and problem statement.
Why is Data Preprocessing Important?
It boosts Data Quality
When examining or receiving raw data, data cleaning is a necessary step to reduce lost or duplicated values or outliers that can result in miscalculated observations. When cleaning data, you can either fill in lost values, remove observations with any missing data, rectify erroneous values, or remove outliers. After cleaning and modifying your data, you provide your model with a reliable data set to find patterns and improve forecasting.
It improves Model Performance
Your model performs more effectively when you have the data in the correct form. When preprocessing data, you can scale values to a specific range of numbers, convert categorical data into numerical values to appropriately train the model, and correct any missing values. All of the above can contribute to reducing inconsistencies and preparing the data in a digestible format for the model, avoiding false positives (Type I Error) and false negatives (Type II Error).
In fact, any model is subject to the scale of the variables it is fit to. Having a handle of large-scale inputs (think of government expense defined in millions, monthly macro aggregates related to the credit or real estate industries, or the number of transactions occurring with credit cards) and combining them with low-scale ones (e.g., financial ratios such as ROE or P/E, performance metrics like asset returns, or sentiment scores from social media, usually in a defined range between -1 and 1 or 0 and 1) will lead to higher importance artificially associated with the larger values by the model.
Moreover, Type I Errors would lead to false discoveries, such as unprofitable trading strategies being detected as profitable, and Type II Errors would miss some profitable opportunities or, in the case of outlier detection in risk management, could miss critical warnings.
Other reasons matter, such as the importance of detecting predictive variables and the focus on stationary time series. We spent years in the industry and developed highly informative signals from forward-looking metrics.
It overcomes Technical Limitations
When provided with raw data, the amount of information and discrepancies can result in anything from overwhelming, highly informative series to noisy, poorly predictive features, impossible to analyze even for the most advanced models. Data preprocessing reduces this data disparity by removing redundant or irrelevant data. And while standard models could manage to work out of the box with small datasets, the real hidden gold resides in large and unstructured databases, whose size could easily surpass your computer’s memory.
It Prevents Bias and Overfitting
When we execute proper data preprocessing steps, we solve problems such as class imbalances in classification problems. This also helps to eliminate biases towards overrepresented classes in your data, thus limiting the risk of overfitting, always lurking in the dark in artificial intelligence applications. A model can score well on training data, but it may fail when unseen data is introduced or evaluated in production settings.
The Bottom Line
Preprocessing is an essential step in any machine learning pipeline. It ensures that the data fed into models is accurate, consistent, and relevant, leading to more reliable outcomes. Before brainstorming on models and hyperparameters, since data quality and volume can vary widely, data preprocessing has become a crucial first step. Organizations that invest in strong preprocessing practices are better equipped to extract meaningful insights and build high-performing machine learning models.