Data preparation is crucial to the machine learning (ML) lifecycle. For ML models to be useful, the data they ingest must be cleaned and formatted in specific ways. The data preparation process transforms raw data into usable data that’s error-free and ensures that ML models accurately serve their intended purpose. In this article, we’ll examine common data preparation issues and the steps involved in preparing ML data for model training and deployment.
Why Is Data Preparation for Machine Learning So Important?
Machine learning algorithms can analyze structured and unstructured data at scale, learning over time how to interpret incoming data and use it to make decisions and recommendations. Raw data may include information such as product names and descriptions, customer contact information, images, videos, social media posts, audio files, and more. Without data preparation, this raw data isn’t usable and it likely contains errors and inconsistencies that will negatively impact the model’s outputs. Although data preparation is a resource-intensive process, automated tools help with efficiency.
Common Machine Learning Data Issues
Algorithms trained with poor-quality data will produce low-quality predictions, insights, and other outputs. Unaddressed, the following four machine learning data preparation issues will skew the accuracy of model predictions.
ML models require large amounts of relevant data to make accurate predictions. When an insufficient amount of data is used, models are likely to show signs of either overfitting or underfitting. Overfitting occurs when a model can’t distinguish the noise in the training data and learns random fluctuations. Models that are overfitting their data perform well on the training data but struggle to generalize to other data. Underfitting occurs when a model simply doesn’t have enough data to capture the underlying trends or relationships in the data. Models that are underfitting perform poorly during training and also struggle to generalize.
Outliers are data points that are an abnormal distance from other values in the same data set. Identifying and resolving these deviations is an important part of the data preparation process. Depending on the nature of the data and its usefulness for model training, outliers can either be dropped or set to the mean value of the feature they’re a part of. In certain cases, outliers can be left as-is, but those cases must be accurately identified.
Many data sets contain duplicate observations. Duplicate data can introduce bias into a model and must be removed to optimize performance.
During model training, ML models require data that’s a real representation of the population for which it's intended. Models trained on data that doesn’t accurately represent the true population will struggle to generalize. By excluding inaccurate variables, unnecessary features, and biased data, data scientists increase the chances of their models performing as expected.
Preparing Data for Machine Learning
Although the data preparation process will vary based on the needs of the organization and its objectives, most teams follow a process similar to the one outlined below.
Define your objective
ML models are designed to solve important business problems. So before starting to prepare raw data, it’s crucial to develop a thorough understanding of the contribution that the model is expected to make. The experience of data scientists and input from key stakeholders will inform how the data is transformed and prepared, increasing the likelihood that the model will perform as expected.
Assemble required data
With the objective defined, collecting the necessary data will be easier. Often this data resides in different silos within the organization and with third-party sources. Gathering this data from on-premises storage, cloud data warehouses, internal applications, and third-party software is often a complex and labor-intensive process. Using a cloud data platform to collect and store all relevant data provides an easily accessible, single source of truth to power machine learning initiatives.
The goal of data exploration is to develop a better understanding of the data that’s been collected. Before model-building, teams must gain context. This involves reviewing information such as the type and distribution of data contained in different variables and the relationships between them. In addition, data visualization tools help data scientists draw more meaningful conclusions by making it easier to spot trends and explore the data more thoroughly.
Data cleaning, transformation, and validation
Raw data contains errors, missing values, and other issues that must be resolved before it's ready for use. The data cleaning process may include changing field formats, ensuring values or units of measure are consistent, and adjusting naming conventions. Once the data has been cleaned, it must be transformed into a consistent and readable format. From there, it’s ready for validation, the process used to verify the accuracy and quality of source data. During this stage of data preparation, data scientists use various techniques to resolve inconsistencies, anomalies, and outliers in the data that can create problems in model training.
Feature engineering and selection
Features are measurable characteristics, properties, or attributes of raw data. Features may include any number of metrics including age, gender, the number of words used in an email, or specific phrases used in digital communications such as online chat, social media posts, or email. ML models use these features to make predictions. Feature engineering extracts the most relevant features from the raw data. During feature selection, data scientists choose the most relevant features to analyze and weed out the ones least likely to be useful. The strategic creation of new variables and selection of existing ones can help improve the model output.
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