Data preparation is a central aspect of AI modeling. It impacts everything that follows it. The quality of data – especially the lack of it – has a rippling effect. Yet, data preparation is the most under-valued and least glamorized part of AI development, leading to this crucial aspect of AI training being overlooked.
This can result in what is known as a data cascade, causing the model to drift. Data cascades are rampant, with more than 90% prevalence. And worse, they are often invisible. The results of this can be dire, not just for consumers but also for the company developing AI systems.
Taking data preparation techniques seriously and doing it well is therefore of extreme importance even as AI becomes integral and dominant. The techniques used to prepare the data (like data enrichment, data cleansing, etc.) can have a significant influence on the quality of the training data and thus the model’s accuracy. Here are some important ones.
Data preparation techniques to ensure the accuracy of AI models
Data quality is a critical determinant of model accuracy, and data preparation is at the heart of it. Computational prowess and advanced algorithms will be rendered futile if the underlying data aren’t properly prepared.
Data preparation techniques involve several steps and various techniques may be used depending on the aim of the AI model. Below are some effective techniques and processes.
1. Data cleansing and validation
AI models are only as good as the data they are trained with. You cannot feed them incomplete and dirty data and expect them to make predictions accurately. A limited, low-quality dataset will tangibly reduce the accuracy of the AI model.
So, collecting relevant data, cleaning, and richly annotating them should be a priority. Collecting a representative sample is important. This will help reduce biases. Additionally, remove redundancies and replace missing values with actual or estimated values. Impute missing values with techniques like mean and median imputation or k-nearest neighbors imputation. And remove outliers or provide context.
Furthermore, reduce the number of attributes if the dataset is sparse or has too many. This can be done by retaining the attributes that have the greatest impact on the data or merging closely related attributes. Normalize numerical values to a consistent range to help the model interpret them more effectively.
2. Data augmentation
Very often the data needed fall short of the data required. Augmenting the available data can help compensate for this.
Data augmentation generates different variations or modified versions of existing datasets to artificially increase their sizes. This is useful in cases where there is a paucity of data or a dearth of diverse data. In the case of images, augmentation may involve adding noise, cropping, rotating, flipping, zooming, changing brightness and saturation, and introducing distortions.
This helps expose the model to a wider range of scenarios and variations that it could (and would) face in real-world applications. By enabling the model to generalize, augmentation makes it more robust and reduces the chance of overfitting.
3. One-hot encoding
Machine learning models can only interpret data in terms of numbers. So, before the datasets are fed to a model, they have to be encoded—i.e., represent them in a way computers can understand. Label encoding is one way to do it. This is, simply put, giving numerical labels to objects—e.g., 1 for dog, 2 for cat. However, this can be problematic as AI models give different weights to numbers, often assigning greater significance to greater numbers. There are other issues, such as the model assuming a natural ordering or relationships where none may exist, leading to inaccurate results.
Source: Medium
One-hot encoding solves this issue by transforming categorical variables into a binary vector representation, where each category becomes a separate binary column. Each category is assigned a single high bit (1) while all others are low (0). Let’s illustrate this with an example.
Consider a one-hot vector used to distinguish each word in a vocabulary from all the others. The vector consists of a single 1 in a single cell used to uniquely identify the word, and 0s in all other cells; and likewise for all the rest of the words in the vocabulary. This ensures that larger words are not treated as more important.
4. L1 and L2 regularization
When you have many features – attributes or characteristics that help a model make predictions – in your dataset or a complex model with too many parameters relative to the amount of training data available, overfitting can occur. The model may learn too well to the point where it begins to notice patterns in noises not indicative of the true patterns in the underlying data distribution, and fails to generalize to new data.
Regularization is one way to prevent overfitting. In particular, L1 and L2 regularization techniques help in identifying and emphasizing the most relevant features in the dataset while ignoring the less important features. L1 regularization minimizes the regression coefficients, in some cases to zero. L2 regularization, on the other hand, limits the influence of any single feature. They help prevent insignificant features from influencing the prediction of the model and thus ensure a more stable fit.
By promoting parsimoniousness and stabilizing coefficients, L1 and L2 regularization contribute to more interpretable models, and helps understand the relationships within the data. They also help deal with multicollinearity by reducing the number of predictors in the dataset with little or no significance.
5. Synthetic Minority Oversampling Technique (SMOTE)
An imbalanced dataset – an unequal and disproportionate distribution of classes – can skew the results of an AI model and make it less accurate. Class imbalance occurs when some classes have significantly more examples than others. It is typical but problematic.
An oversampling method called SMOTE can help address the class imbalance problem by increasing the representation of the minority class from the existing samples. This is done by duplicating existing data as well as generating new data that contain values close to the minority class. The synthetic examples are created by interpolating existing data points of the under-sampled class.
While SMOTE is a valuable technique for handling class imbalance, we should be mindful of potential overfitting that could ensue if the synthetic samples generated are oversampled. It should therefore be used judiciously, taking into consideration the specific characteristics of the dataset and the problem at hand.
6. Feature engineering and feature selection
Features provide information needed for the model to learn, identify patterns, and make predictions. However, not all features are readily available or of equal significance. Feature engineering is a way of creating and extracting relevant features or modifying existing ones in a dataset to improve the accuracy and performance of a model.
The process involves, using raw data, the creation, extraction, transformation, and selection of features. With this technique, we can create new features by mixing existing features, extracting meaningful information from existing data, and transforming the data into more suitable formats for machine learning algorithms. Feature selection involves picking the most relevant features, and removing redundant, irrelevant, or noisy features.
Source: Javatpoint
Feature engineering can help improve the model’s accuracy and interpretability and address overfitting, underfitting, and high dimensionality.
Well begun is well done
To have a good and efficient AI model, it needs to be trained with high-quality relevant data. This requires meticulous data preparation techniques. If this is not done properly, then all subsequent stages will be splotched. Contrarily, well-prepared data will go a long way in influencing the quality of the model. But, achieving this in-house consumes high time and effort investment.
Investing in data annotation, data cleansing, and data enrichment services provided by third parties can make this otherwise tedious task more efficient and reliable. Ensuring that AI models perform accurately, with minimal biases, is highly important in an age where key decisions with consequential outcomes are made by them or with them. The impacts of AI, in size and magnitude, will only increase. Data preparation techniques play a significant role in shaping these impacts and ensuring that this aspect is not overlooked is a necessity.