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2022-02-14 14:10

Building a Training Dataset in Machine Learning: A Comprehensive Guide

Building a Training Dataset in Machine Learning: A Comprehensive Guide

Introduction

Computer Vision is a field of Artificial Intelligence dedicated to imitating the behaviors of the human visual cortex. It involves various procedures such as the Image Classification task, Object Localization task, Object Detection task, and Object Segmentation task. These are all meant to detect and recognize objects in visual environments.

Machine Learning algorithms applied to Computer Vision are the tools that enable such a process. But to do so, they need to be trained as human beings would be, on training datasets.

What is a training dataset?

In Machine Learning, training data is the data that will be used by the system to acquire the knowledge it will need when processing inputs. Machine Learning or ML algorithms are like children discovering their environment; they need to be taught and trained in order to acknowledge what surrounds them. After being trained on an object recognition training dataset, they are able to recognize objects which are introduced into their world.  

A necessary step to guarantee Machine Learning accuracy

The true goal of using ML algorithm in Computer Vision is to detect, recognize and classify objects within an image, as a human being would. Consequently, the method has to be as accurate as possible. To guarantee its accuracy, the model has to go through a training phase. The training needs to be done with a clean, complete, and accurate set of input data. This learning set is supposed to teach the algorithm what is expected from it. The ML model is going to train on this set for a certain time until the correct information is gathered and delivered by the machine. Over time, the latter will then be able to comprehend the features of the data it is submitted to and to adjust itself in order to match the expectations of users.

Based on the type of ML algorithm being used, the training process will show some variations.

Training dataset in Supervised and Unsupervised Learning

Supervised ML algorithms function with data that is already labeled. Each sample is tagged with one or several labels corresponding to a category of objects. It corresponds to specific characteristics, and properties, which allow the identification and classification of objects. According to the labeled data the system is learning from, it will be able to use these pieces of information to identify and classify new images. Labeling data is often done by the human hand; it is known to be a long and complex process, but it allows the program to be very accurate.

Unsupervised ML algorithms work with unlabeled data. This means that the data samples are raw and are not affected by a specific tag. The process is more challenging for the model, as it has to identify similarities in pixel patterns in order to allow it to identify objects in one or several pictures. The model has to analyze and evaluate characteristics in each image such as shapes and colors for example. With unsupervised machine learning models, the main difficulty relies on the fact that data is not labeled. They have to train on raw data and know-how to differentiate characteristics and patterns in a picture in order to analyze and detect objects in new sets of data.

Semi-supervised learning solutions are being more and more used for Artificial Intelligence tools. This kind of method uses a combination of labeled and unlabeled data. The training phase is then based on a hybrid set of data and the machine has to learn how to leverage the explicit knowledge of the labeled data and the implicit patterns of the unlabeled data.

How to ensure the quality of a training dataset?

Training data in ML algorithms is a crucial element you should not neglect if you want your application to be accurate and efficient enough in its predictions. To guarantee this accuracy, it is necessary to build up a high-quality training set.

The main characteristics for high-quality training data

The higher the quality of your set is, the better it is for your model to train on. It gives the algorithm the best tools to work on when starting the training phase.

First, your training data needs to be relevant. For example, if you want your system to analyze images and evaluate the progress of tumors, it will be more relevant to download a set of X-Rays input labeled with the boundaries of the tumor in order to train your model. You need to provide the system with a training set that fits the goal that you are looking to achieve. If you train your program on inputs that have nothing to do with the predictions you want it to make, there is absolutely no doubt that the results will not be acceptable or accurate.

To reduce the bias of your AI program, you must introduce it with inputs that are meant to be analyzed by your algorithm. It has to be representative of what you want it to detect and predict. A huge variety of inputs is necessary. The more important dataset you have, the more accurate your model will be. For example, if you want to detect faces to count the clientele of your shop, you must keep in mind that a lot of different faces are prone to come into the store. As a consequence, it is crucial for you to introduce training data that contains faces from different countries, ages, and gender. Training your program on this kind of extended data is extremely important if you want your AI solution to be fair, effective, and satisfactory.

The importance of Human’s involvement in preprocessing the training dataset

Getting high-quality training data is crucial. But it needs to be checked and preprocessed before using it, and this is where human people intervene.

Using a Supervised learning approach involves the intervention of the human hand. Before submitting datasets to a supervised learning program, a data scientist needs to work on the labeling and cleaning of the data. Depending on its use, it is necessary to format your set. That way the model will be able to function faster than if it is disturbed by any polluting information it does not have to evaluate.

Preparing the data also means enlarging as much as possible the library by doing some data augmentation. For picture inputs, it can be done with image blurring, changing their orientation, translating the pictures, or even making them black and white. The goal of data augmentation is to give the system even more inputs to train on. This step really raises the chances to get accurate responses from the model.

The training phase is on and the first outcomes are available. At this moment, data scientists may also take action in order to check if these are satisfactory or not. If the training predictions are matching the outputs you are looking for, the model can go through test and validation phases, with different datasets. If the predictions are different from the ones you are expecting, the data scientist must have to go back to the training phase and modify some of the parameters previously set, before going through the next step.

Training, test, and validation data: what’s the difference?

Training data is basically the reference used to train the model.

Test data is different from the training data set. It is meant to evaluate the performance and/or accuracy of the ML algorithm, after having trained it. The set uses data that the machine has never encountered before. The goal is to make sure the machine learning model has not been over-optimized on the training dataset.

In contrast, validation data is used to evaluate the training. This is again a new set of input, made to help the model protect itself from overfitting. It is used for intermediary evaluation and parameter tuning (for example, to optimize the size of a neural network). Usually, data scientists take a part in training data and keep it as validation data.

The main difference between test and validation data is the moment when it is being used. Validation takes place during training, it gives the evaluation of the model on unseen data. And the test dataset is used when you want to evaluate the performance at the end of the machine learning design process. It is not used for performance optimization and only gives indications on whether the model is functioning correctly or not.

If you happen to have a limited amount of data in your training set, you might want to proceed with a technique called cross-validation. Data scientists can decide to divide training data into several subsets. One of them should always be kept apart to execute the validation phase.

Like any other element of Artificial Intelligence solutions, training data is a critical element that should not be neglected when you design a machine learning model.

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