The dataset that you use to train your machine learning models can make or break the performance of your applications. For example, using a text dataset that contains loads of biased information can significantly decrease the accuracy of your machine learning model.
This was what happened to Amazon’s initial tests. They trained a machine learning model for their automated hiring system. This tool was designed to pinpoint the best candidates across a batch of applicants for the job vacancies at their engineering departments. However, because they used the wrong dataset, their algorithm produced results that were considerably biased towards male candidates.
Impact of Bad Training Datasets Should Not be Underestimated
There are also many instances where using the wrong dataset can be quite dangerous. As an example, let’s use a machine learning model for reducing healthcare costs and improving in and out-patient treatment procedures across a certain geographic region. Now imagine training this model with a dataset that contains relevant medical records of patients with highest and lowest death risk percentages throughout the last several decades.
Remember, information about patients with the highest death risk after contracting pneumonia complications like asthma is crucial for the accuracy of this model. Without this data, medical practitioners and hospitals that rely on this machine learning model are likely to make erroneous diagnosis. Worse, they can unknowingly provide harmful advice.
Steps for Preparing Good Training Datasets
1. Identify Your Goal
The initial step is to pinpoint the set of objectives that you want to achieve through a machine learning application. This can help you identify salient points like the most suitable model architecture, training techniques, data collection, dataset annotation, and performance optimization methods to use for resolving problems relevant to your main challenge. Keep in mind, your mission is to cost-effectively hit your goal.
For example, if you need to automatically translate words, phrases, sentences and paragraphs from Italian to British English, then you’re best bet is to develop an NMT (neural machine translation) tool. Because this requires large neural networks trained through huge volumes of data and powerful computers, then you’re recommended to take a closer look at the most effective deep learning methods that are often used for developing an NMT application.
2. Select Suitable Algorithms
different algorithms are suitable for training artificial neural networks. So you need to pinpoint the best architecture for your model.
We will only cover general categories of these algorithms and also list down specific methods. This is to ensure that we remain focused on the topic that we’re covering right now, which is creating datasets for your machine learning applications. So let’s start with the general categories of these algorithms:
- Supervised Learning
This is where a function is generated from pairs of dependent and independent variables. The former are target variables. Meanwhile, the latter are predictors that make inferences to identify the targets. Through this function, inputs are mapped by the model to desired outputs. When the model achieves the desired accuracy or stops learning, training is discontinued. The most common algorithms that are used for supervised learning approaches include linear and logistic regression, random forest, and decision tree, among a few others;
- Unsupervised Learning
Algorithms that fall under this category do not have target variables. This means a model that uses an unsupervised learning approach is not fed with a dataset that aims to aid it in mapping inputs to specific outputs during training. This is why these algorithms are commonly used for automatically finding and extracting hidden features from a dataset, and also for segmenting large data clusters into particular groups. The K-means algorithm is a popular choice for these artificial neural networks; and
- Reinforcement Learning
This is where a model consists of an agent and an environment. It’s given a set of decisions, which is designed to help the network learn from the results of its choices. Each of these decisions affects the result of the next choice. Simply put, these nested choice sets have a variety of effects to a nested set of outcomes. These are at particular points where the model has made a certain number of choices. This means the model learns to make the best decisions through trial and error. This is widely used for digital video games and also for the chat bots of a website’s help section. Well-known algorithms that are used for reinforcement learning include Q-Learning, DDPG (deep deterministic policy gradient) and others.
Meanwhile, machine learning and deep learning algorithms that fall under these general categories and are widely used today include the following:
- Linear Regression;
- Logistic Regression;
- SVM (Support Vector Machine);
- Naive Bayes;
- kNN (k- Nearest Neighbors);
- Random Forest;
- Decision Tree;
- Dimensionality Reduction algorithms;
- Q-Learning; and
- Gradient Boosting algorithms.
3. Develop Your Dataset
Once you’ve chosen the most suitable algorithm to train your artificial neural network for your use case, you’re ready to create your dataset. This involves a number of steps. Remember, this is crucial and can significantly affect the overall performance, accuracy and usability of your machine learning model. So now, here are the steps to build your dataset:
- Determine Cost-Effective Data Collection Strategies
There are many public and private groups like universities, social organizations, companies and independent developers that offer paid or free access to their datasets. Some of these are open sourced under particular licenses, while lots are available as premium standalone datasets, or bundled by SAAS (software as a service) platform operators with their subscriptions.
In most cases, you’ll be able to determine the best strategies for creating your own datasets through these open source and premium content materials. For example, if you’re developing a device that’s integrated with an ASR (automatic speech recognition) application for your English-speaking customers, then Google’s open source Speech Commands dataset can point you to the right direction.
Depending on the license of the open source and premium datasets that you find suitable for your use case, you can also augment those with your own data. Keep in mind, what provides the best value in terms of model accuracy and performance is usually a custom dataset that’s specifically designed for your particular application.
- Identify the Right Dataset Annotation Methods
At this point, you likely have a dataset that contains a combination of open source, premium and custom content. This means your next step is to ensure that your entire dataset is annotated properly.
For example, if you have an image dataset that you want to use for training your computer vision application’s deep learning model, then you need to decide whether to use bounding boxes, semantic segmentation, polygonal segmentation, or others to annotate the digital photos in your dataset. Meanwhile, if you’re creating an NMT program, then your text dataset should be annotated through entity annotation, text classification, sentiment annotation, or a combination of these and a few others.
- Optimize Your Dataset Annotation & Augmentation Workflow
A dataset annotation tool can enable you to reduce the time and expenses that you need for preparing your dataset. Kili Technology provides a platform where you can outsource these requirements to both your in-house staff and remote workforces. This multi-purpose annotation tool can also enable your management team and quality assurance specialists to monitor the output of your virtual agents and in-house employees.
By optimizing dataset annotation tasks, you’ll be able to dedicate unused resources for augmenting your datasets with more useful content. This in turn can enhance the performance and accuracy of your machine learning applications.
- Clean Up Your Dataset
Consistency is important in enhancing the effectiveness of your dataset for training your machine learning model. This means you need to make sure that the content of your datasets are in a similar format, has minimal unnecessary noise (yes, noise is sometimes helpful for certain use cases), and only contains annotated features that are significant in helping your model learn during the training process.
You’re also advised to substitute missing values in the features of your annotated dataset with the most suitable assumed values as dummy data points for your use case. Remember, missing data points can negatively affect the accuracy and performance of your machine learning application. For example, if a considerable percentage of the files in your dataset do not have appropriate numerical values, then you can try to use mean values. On the other hand, if you’re trying to solve a classification problem with your model, then you can try using the most frequently used classes closest to the missing categories of the files in your dataset.
- Closely Monitor Model Training
By carefully studying training logs of your machine learning model, you’ll be able to properly adjust the myriad of ways that you can do to clean up your dataset. Plus, you’ll be able to re-configure the hyperparameters of your model architecture and training algorithms.
This can also allow you to optimize and clean up your dataset through your annotation tool in more straightforward ways. That’s because you’ll be able to create a new set of guidelines for your in-house staff, managerial team and remote workforces. Check out Kili Annotation Tools!!!