When building a supervised ML model, it’s important to have a quality training dataset. Whether it is a classification model, logistic regression, or neural network – without following the right procedures related to training and testing datasets, a data science project can be set up to fail.
This post is intended to serve as a guide for those data scientists who want to ensure their supervised learning algorithm avoids common ML pitfalls and runs effectively with training and testing data by following the appropriate procedures for each step while building the predictive model.
What is Quality Training Data?
Because of the dependencies mentioned above, it’s critical that the data scientist working on a project is sure they have quality training data. But this also begs the question, what is quality training data?
Labeled vs Unlabeled Data
One can think of the training dataset as the “food” for the machine learning model. Typically a data scientist will do a 70-30 train-test split to allow a large portion of the labeled data for training the model and 30% for testing its performance.
One must strike a balance between quality and quantity when it comes to training data. This is because, in the world of artificial intelligence, more data is almost always better than less data. The algorithm thrives on the training data because that is how it learns.
In essence, by providing a pre-labeled dataset, one tries to determine the computations and order in which they need to be performed to arrive at the target value. The hope is that by finding patterns that accurately describe the training data one can repeat these steps on the validation and testing data and arrive at comparable results.
A Brief History of Data Labeling
There are two major assumptions a data scientist must make regarding training data, and if these assumptions aren’t met the model will perform poorly:
1. The Training Data is Valid.
The label of each input is correct. If there are mislabeled data points, through manual data entry issues or biases in the collection process, the training data is no longer a valid tool.
2. The Dataset is Reliable.
The sample from which the training data is collected acts as a generalization (accurate representation) of the population as a whole. For instance, random sampling, or random stratified sampling was used when selecting a portion of data from a larger population.
If this doesn’t hold true, the machine learning model may perform well during validation, but when presented with new unseen data, the model will fail.
It’s clear that quality training data is necessary, but what is less apparent is the proper way to go about getting the data, transforming the data, and training the model. This is clarified in the sections below:
ML DataOps and High-Quality Training Data
Multiple factors play into whether a project uses quality data for its training set. The subsections below explore the impact of the business processes and decisions, annotation tools, and people skill development on training dataset selection and preparation.
Collectively, these three factors can be referred to as ML DataOps. By reviewing each component in detail, it is easier to understand the use cases for quality training data.
1. Business Requirements and Definitions
Before diving too deep into a data science or deep learning project, one must define the business requirements. If a company is tackling some common use cases like developing a classification model for customer churn, one must first define churn, identify which factors can be used as predictors, and decide whether it is more important to prevent false positive results or false negatives – among many other considerations.
This directly impacts the training data selection and ultimately any new data that will be fed into the model.
Supervised Learning vs. Unsupervised Learning
When does the data scientist need to do a train-test split? This comes down to supervised vs. unsupervised learning algorithms and this is also grounded in the business decisions and definitions.
While unsupervised learning models like k-means clustering, hierarchical clustering, or Principal Component Analysis (PCA) deal with unlabeled data, supervised machine learning algorithms require model training through the use of input data and validation data.
For instance, if a data scientist was working on a neural network, it’s necessary to provide the features for the model, as well as the labels or outcomes to determine the model’s performance.
Data annotation can be a very tedious, manual process. Proper data labeling is necessary for training an algorithm because if data scientists feed the model false information, they cannot expect accurate and reliable results when new data enters the model.
Luckily, tools exist for making data annotation processes more efficient including transfer learning, 3D point cloud data annotation, and automatic multi-point selection via Bounding Boxes.
Who are the data scientists behind the machine learning project? Depending on their knowledge, skills, and abilities, these individuals affect how much training data is available for a project, the quality of the data, how the raw data is transformed, as well as which metrics are used to assess a model’s performance.
This is why companies require a sort of people platform to ensure that all team members are trained, constantly developing their skills, and optimizing their workflow.
Once it is clear that the people, processes, and tools are in place, the quality training dataset can finally be used to train the model, allowing for multiple iterations of optimization and fine-tuning.
The Validation Dataset and a Model’s Performance
Of course, even after feeding the model a large dataset for training, data scientists need to validate their results and measure the model’s performance. By presenting the machine learning model with new, unseen data one can determine whether it fell prey to problems such as overfitting or underfitting.
On the other hand, another problem that can occur is underfitting. When a model underfits to the training data, it is unable to learn the relationship between the features and the target. The best way to overcome this problem is to revisit your training data. Whether this involves collecting more data, cleaning the data, and adding new combinations of features that capture the variance, having quality training data might save an underperforming model.
When the model looks like it is performing very well during training, but fails to hold up to the testing dataset, this means it is overfitting to the training dataset. One way to address this is to cut down on the model’s flexibility. If the algorithm introduces too many combinations of features that are specific to the training dataset, it will likely fail to generalize to the new unseen data.
One Step Closer to the Predictive Model
In conclusion, this post aimed to cover quite a few key areas:
- What is training data?
- When is it necessary?
- How does it impact an ML model?
While there are many more questions to ask and actions to take when developing a successful model, the next step is simple – ensure that the data science team has the tools that make up the foundation of the machine learning process.
An easy way to do this is to rely on a data service company, like iMerit. We specialize in machine learning, automation, and artificial intelligence for companies of all sizes. Our data annotation services and people platform can bring you one step closer to a winning predictive model.
And that’s the (data) point.