Training data or a training dataset is the initial data used to train a machine learning or artificial intelligence model. Machine learning algorithms learn to process information and recognize patterns with the help of training data.
A training dataset is, therefore, the fundamental learning unit for a machine learning model. That’s why the quality of the output you receive from an ML model highly depends on the training data. How does training data work for machine learning algorithms, though?
In this article, we explore the role of training data in machine learning. We will also explain the three types of training data and how integral they are to any ML algorithm’s performance.
Let’s dive in.
How Does Training Data Work for Machine Learning Models?
Machine learning algorithms improve their performance over time as you feed them more data. That’s a basic tenet of AI. More data more likely than not equals better results.
This introduction of data to an ML model is known as training. During training, the algorithm is given a set of examples called training data. Training data helps the machine learning model to recognize, classify, and correctly label distinct elements.
An excellent example is AI models that recognize and classify vehicles on roads. Training data will help the particular model in this case to tell if they are trucks or cars.
That’s how machine learning models can tell vans from other vehicles on the road. Enough of the training data will ensure the Ai systems don’t struggle to recognize or classify the unique elements.
The training data comprises input data and the correct output or label. For instance, if training an algorithm to recognize opinions, you’ll need to have something like this:
|I love the new AI model
|However, sometimes, it lags too much
The algorithm uses this data to learn the input and output relationships and make predictions on new, unseen data.
Types of Training Data
There are three types of training data that work for machine learning models:
a) Structured Data
This type of data is usually quite organized. Data from sales transactions, addresses, and stock information is structured and thus searchable. That’s because most of this data you can find in a database.
b) Unstructured Data
Another name for this kind of data is qualitative data. The data usually has no organization, as the name suggests. Such data is typically difficult to determine. An example of unstructured data is social media information.
Being highly unstructured doesn’t stop the data from being used as training data.
c) Semi-structured Data
Semi-structured data is in between the first two data types. Depending on the model you are working on, semi-structured data can be just as helpful as the other training data types.
What Is the Importance of Training Data to Machine Learning?
Training data is essential to machine learning since it teaches algorithms to recognize patterns and decide scenarios. The reasons below explain why training data is vital for machine learning.
1. Validating Machine Learning Models
Some think all there is to developing a machine learning model is feeding it mountains of data. That, however, shouldn’t be the case if you’re to get a machine-learning model that will produce accurate results.
Enter data validation. Specific training data helps to validate the data an ML model learns to ensure zero errors. How does training data enable that?
There is training data specifically for testing the accuracy of the machine learning model. For instance, if the ML model should identify specific scenarios, validation data will try to see if the results are authentic.
If they aren’t, then there will be an issue with the machine learning process. That could affect the predictions the model makes since they most likely will be unreliable.
2. Offering Key Inputs for ML Algorithms
A machine learning model’s critical decision-making heavily depends on training data. Training data helps sort through the unimportant input vital in making an algorithm work perfectly.
That’s why proper data labelling is integral to any supervised learning process. The key inputs in training datasets help machine learning models relate data accurately to real-life situations.
3. Training Data Helps Come Up with Testing Data
Testing data is the data that fine-tunes the machine learning process. After feeding the algorithm training and validation data, it is testing data that will show whether it can work perfectly in real situations.
Training data helps develop this data that’s integral to the machine learning process.
4. Organizing Unstructured Data
This is one of the essential roles of training data in machine learning. The data is at first unstructured or jumbled up. Such information isn’t helpful to the training of a machine learning algorithm.
Feeding the algorithm jumbled up data will have it only produce inaccurate results. Hence, training data helps organize this unstructured data into something understandable for the algorithm.
That will help avoid garbage in-garbage out scenarios with the algorithmic model.
Another use of training data is to prevent over-fitting when an algorithm performs well on the training data but poorly on new, unseen data. This can occur when the algorithm is too complex and has learned patterns in the training data that do not generalize to the broader population.
To fend off over-fitting, it is often helpful to use a technique called regularization. The method involves restricting the algorithm’s complexity.
Factors To Consider When Selecting Training data
Results should form the basis of the training data you select. With that in mind, several considerations exist when selecting and preparing training data for a machine learning algorithm.
1. Data Representation
The data you use should represent the problem the algorithm is trying to solve. For instance, if training the algorithm to identify cats in photographs, the training data should include a diverse set of images.
Those images should accurately reflect the cats the algorithm will encounter in the real world. Suppose you only want the algorithm to deal with Maine Coon cats. In that case, the data should represent only Maine Coons.
2. Proper Data Labelling
Proper labelling is integral to the accuracy of the algorithmic model’s results. In supervised learning, the labels must be accurate and consistent when the algorithm is given both input data and corresponding output labels.
Incorrect or inconsistent labeling can lead to poor performance and bias in the algorithm.
3. Size of the Data
The size of the training data will also have a considerable impact on the algorithm’s performance. More data leads to better results, as the algorithm has more examples to learn from. However, you’ll often get to the point of diminishing returns, where adding more data does not significantly improve the algorithm’s performance.
4. The Quality of Data
The quality of training data is everything we have discussed above and more. According to Manmeet Singh, a machine Learning Lead at Apple, the training data must be high quality for the algorithm to learn anything relevant.
Training data works in various ways in machine learning. One common approach is splitting the data into a training set and a validation set.
The training set teaches the algorithm, while the validation set evaluates the algorithm’s performance.
While evaluating performance, the validation set also fine-tunes the parameters of the algorithm’s performance.
5. Uniformity and Relevance
Uniformity refers to the attributes of the data source. For the best results, all training data should be from the same source with the same characteristics.
Relevance is also an essential factor to consider. If you want a model that analyzes social media usage, you’ll need data relevant to social media.
That will have to be data from all the popular social media sites, including Facebook, Twitter, Instagram, and others.
Frequently Asked Questions
1. How different is testing data from training data?
Training data is the initial data you feed a machine learning system. Testing data is the data you’ll use to determine the accuracy of the machine learning model.
Machine learning systems need the testing data after all the phases of data feeding to determine their accuracy in real-life situations. If it isn’t as accurate as you want it to be, you’ll need more training data.
2. Does unsupervised learning use labelled data?
Only supervised learning requires the use of labelled data. Semi-supervised learning uses both unlabeled and labelled data.
Training data is a crucial element of machine learning since it helps to teach algorithms to recognize patterns and make decisions.
Properly selecting and preparing training data and using it effectively during the training process is essential for developing high-performing machine learning algorithms.
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