Data has become one of the key backbones for all types of industries. Companies today rely heavily on data to perform a wide range of functions, including building up their business and making it more successful. Helping businesses use their large datasets to their maximum potential is artificial intelligence (AI) and its various branches like machine learning (ML). Apart from being crucial in helping businesses make key decisions, data is also vital for automated systems like machine learning, natural language processing, etc., to perform to the best of their abilities.
Using machine learning models, businesses can automate various operational processes as well as gain deep insights from various types of text data, including, emails, documents, social media, surveys, support tickets, etc.
But the actual success of these models depends on the quality of data that you provide. No matter how robust your machine learning models are, if the data they are trained on is not correct, adequate, or relevant, they will not serve the purpose. Irrespective of how efficient your machine learning algorithms are, without quality training data, they will just fail. Hence, it is not surprising to see businesses focusing on providing quality training data so that their machine learning models can do the job successfully.
This need for high-quality training data starts in the initial phase itself as it helps in setting up the path for the future. In this post, we will dive in-depth to understand all about training data, its usage in machine learning, some of the factors that affect the training data quality, how to get the training data, and more.
Table of Contents
What is Training Data and Machine Learning?
Training data, as the name implies, is the primary dataset used to train the various machine learning algorithms. Your machine learning models can create and refine the rules using this data. This dataset is also known as the learning, training, and training sets. The training data is one of the most important parts of any machine learning model, allowing it to perform various tasks and make accurate predictions. These models continuously analyse the datasets to understand their characteristics and make changes to ensure high performance.
Below is an example of what the training data should be if you want to train a sentiment analysis model (to understand sentiments like positive, negative, and neutral).
Input: The new interface is amazing! Output: Positive
Input: The new interface is slow. Output: Negative
Using training data involves human efforts to some extent. The amount of human effort depends on what kind of machine learning algorithms are being used and what issues will they help in resolving. No matter how robust and sophisticated a machine is, it cannot completely mimic the way humans perform. Hence, unlike humans, machines require several hundred examples to be able to identify patterns, emotions, sentiments, etc., from the various forms of training data. Since training datasets include texts, images, numbers, videos, audios, etc., in various formats like XML, PDF, HTML, etc., it is important to ensure that your machine learning models are receiving the relevant and accurate training data.
However, once your machine learning algorithms have the right training data, they can perform far more accurately and timely than humans.
The training data in itself can be categorized into two groups – labeled data and unlabeled data.
Labeled Data: Also known as annotated data, this type of training data is used in supervised learning. As the name suggests, it is a group of training dataset samples that are tagged using meaningful labels. These labels help in identifying the data’s classifications, characteristics, properties, etc. When you label your training data, it helps to train your machine learning algorithms and ensure that the models are able to predict the right outcome. For example, the images of different flowers can be tagged as roses, tulips, daisies, sunflowers, etc. The machine learning model can use this labeled data to understand the characteristics of different flowers and group them accordingly.
The process of labeling data involves human efforts and is time-consuming which also means that it is an expensive process.
Unlabeled Data: As the name suggests, these types of data are the ones that are not tagged with any labels which can help in identifying the data’s characteristics, properties, classifications, etc. This type of training data is used in unsupervised learning wherein the machine learning models have to identify patterns on their own to provide the right outcome. So, if you apply this type of training data to our example above, the images of the flowers will not be labeled. Instead, the ML models will have to analyze each image using characteristics like color, shape, etc. Once the models have analyzed a sufficient number of images, they will be able to differentiate any new images and categorize them into flowers like roses, tulips, etc. Even though the ML model does not know that it is a rose, it will be able to identify depending on the characteristics.
Apart from these two categories, some machine learning models also use a hybrid model which involves both supervised as well as unsupervised learning.
Use of Training Data in Machine Learning
So, how is training data used in machine learning? Well, traditionally, the programming algorithms usually follow a pre-defined set of rules and instructions for receiving the input as well as providing the output. Each and every action is rule-based without any dependency on historical data. As a result, these traditional programming algorithms do not improve as time passes. Machine learning algorithms on the other hand are the exact opposite of this!
Historical data plays a key role in machine learning. Similar to how we human beings depend upon our past experiences to make decisions, machine learning models also depend on their training datasets with historical data to make predictions, such as classifying images or understanding the intent/sentiments of a sentence, etc. Hence, it is vital that the training data is updated periodically with new information.
As mentioned earlier, having incomplete or irrelevant training datasets can hinder the performance of your machine learning models. This is why it is best to ensure that you are providing high-quality training data, labeled and annotated so that your ML algorithms can provide you accurate output. Along with quality, the quantity of your training data also makes a huge difference. For example, if you trained the ML models using training data from 100 interactions, it will obviously be inferior to the ones for which you provided 10,000 interactions.
Also, the process of providing training data is an ongoing process since it is based on real-time conditions. So, in order to ensure that your training data remains effective throughout the machine learning development lifecycle, you need to keep updating and retraining your datasets.
Types of Data
There are three major types of training data used in building machine learning models, each with its own role and importance.
- Training Data – Without a doubt, this is the most important dataset that helps in setting up your machine learning models and helping them make accurate predictions. It amounts to more than 70% of the total data used by your ML models.
- Validation Data – As the name implies, this is a dataset which is used to validate the ML model during the training period. Your ML model may not necessarily ‘learn’ anything from this type of dataset; however, it does help the model in ensuring that it is not underfitting or overfitting. This type of dataset is also sometimes referred to as dev set or development set.
- Testing Data – The final type of data is the testing data which helps to test the performance, accuracy, and prediction capabilities of your machine learning model. It basically contains a sample of the data to evaluate how well the model fits on the training data. Some people use validation data and testing data interchangeably as well.
Key Features of Good Training Data
The importance of having quality training data cannot be emphasized enough. The entire success of your machine learning model depends on the quality of the training data you provide as inputs. Here are some of the key features of good, quality training data.
- Relevance – Having relevant, up-to-date training data is one of the key features. So, if you wish to automate your customer support processes, it would be ideal to have a training dataset with real-time customer support data.
- Uniformity – It is recommended that a good training dataset should be uniform with regards to its source and attributes.
- Comprehensive – The more dataset you provide, the better your ML model will perform. So, you need to ensure that your training dataset covers all the scope.
- Diverse – The training dataset should be handled by those who are not biased as it will impact your outcome.
- Representative – The data points and factors of your training data should be similar to the data that will be analyzed.
Factors Affecting the Quality of Training Data
Since the machine learning models are completely dependent on the training datasets, you need to ensure that you have a fair understanding of the factors that affect the quality of the training data. This will help you to overcome any issues and provide competent and favorable training datasets. Here are the top three factors that affect the quality of training data.
- People – As established earlier, providing labeled training data is highly recommended and this involves human efforts. The people involved in training the ML models have a high impact on its overall performance and accuracy. Human beings tend to be prejudiced and biased and this can affect the way they label the data which in turn will affect the way the ML models function.
- Processes – In order to ensure the quality of the training data, it is vital that the data labelling process is quite robust and undergoes sufficient quality control checks. This is one of the best ways to ensure that your training data is high quality.
- Tools – Today, there are several advanced data labelling tools available in the market. So, ensure that you do not depend on any outdated or incompatible tools as they will have a negative impact on your training dataset’s quality. Using the modern tools will not only ensure the quality but will also reduce the time and cost involved.
How to Get Training Data?
There are several ways through which you can get your training data. The source primarily depends upon the scope of your machine learning project, budget, the timeline of the project, etc. Below are the three primary sources through which you can get your training data.
- Open-source training data – Businesses who cannot afford to spend big bucks on data collection, labelling, etc., rely on open-source training data, such as Google Dataset Search, Kaggle, and ImageNet. This is one of the easiest ways to collect your training data as it is readily and freely available. However, you will have to reannotate these datasets slightly to ensure that they fit your requirements.
- Internet and Internet of Things (IoT) – Mid-size companies often rely on internet and IoT devices for collecting training datasets. Unlike open-source datasets, this method focuses specifically on collecting data that matches exactly to your machine learning model requirements. Businesses can collect raw data from sensors, cameras, etc., and then clean, standardize, and annotate it.
- Artificial training data – Also known as synthetic data, it is artificially created data which requires a lot of time and large amounts of data processing resources. This is the preferred method if you are looking for high quality training data with the exact features that you need for training your machine learning algorithms.
Conclusion
We hope that by the end of this post, you have a clear understanding of what training datasets are, how they are used in machine learning, what are the different types of data, the source of these training datasets, their features, and factors affecting their quality. As mentioned earlier, businesses today are completely dependent on data for various reasons, and with machine learning and artificial intelligence here to stay, you need to be aware of how to use all the large and complex datasets to your advantage.
Here are some of the top machine learning software for you to check out! You can also visit SaaSworthy to know more about other tools and technologies useful for your business.
