The training dataset that you use to develop your machine learning models can affect their accuracy and performance. For instance, if the dataset contains a lot of biased information, it can decrease the accuracy of your model.
Amazon's initial tests with its hiring system resulted in results that were significantly biased against male candidates. The company used the wrong dataset to train its machine learning model, which was supposed to identify the best candidates for its engineering vacancies.
Don't Underestimate the Effects of Bad Datasets
Using the wrong dataset can be dangerous and lead to errors in the development of machine learning models. For instance, if you're planning on using a machine learning model to improve the efficiency of healthcare facilities, you might want to use a dataset that includes medical records of patients with the lowest and highest death risk percentages.
Tips For Creating Good Datasets for Training
1. Identify Your Goals
Before you start working on a machine learning project, you must identify the main goals that you want to achieve. This step can help you identify the various components of the project that will help you achieve these goals. Doing so will allow you to focus on the most effective ways to implement the model. Your key focus should be to hit your goal cost-effectively.
2. Determine Suitable Algorithms
You must identify the various components of the project that will help you achieve the goals that you set. The most important factor that you should consider is the architecture that will allow the machine learning model to train. Some of the more common categories include:
- Supervised Learning
A function that's generated from pairs of independent and dependent variables is known as a target function. The former is used to identify the targets, while the latter makes inferences to the model. When the model reaches its desired accuracy or stops training, the model is discarded. Some of the most popular algorithms that are used for training are random forest, logistic regression, and linear and logistic regression.
- Unsupervised Learning
Unsupervised learning algorithms that do not have target variables are often used to train models that are designed to map inputs to specific outputs. This is because these algorithms are commonly used to find hidden features in a dataset and perform other tasks related to data classification. One of the most popular algorithms that are used for this type of training is the K-means algorithm.
- Reinforcement Learning
A model is usually composed of an agent and an environment, which is designed to help it learn from the results of its decisions. Each of the decisions that the model makes affects the outcome of the next choice. This is because the decisions that the model makes have a variety of effects.
The goal of reinforcement learning is to help the model make the best decisions based on the results of its trial and error. It is commonly used in digital games and for the chatbots in a website's help section. Reinforcement learning algorithms that are used for this type of training include Q-learning, DDPG, and random forest.
3. Create Your Dataset
Before you start training your artificial neural network, you must choose the appropriate algorithm that will train it. This process can take many steps. This step impacts the overall performance, usability, and accuracy of the machine learning model.
To build this dataset, you will need to determine cost-efficient data collection methods, identify the best annotation methods, clean up your datasets, optimize your augmentation workflow and consistently monitor your dataset training models.
Before you start training your machine learning model, it's important that you thoroughly study its training logs to ensure that everything is running smoothly. Doing so will allow you to adjust the various steps in the process and improve the efficiency of your model. This step can also help you clean and improve your dataset by adding new guidelines to your staff members and remote workforces. This will allow them to easily follow the steps in the processes.
The performance of each of your applications greatly relies on the datasets used for training the AI. Using the wrong datasets can create an unreliable and inaccurate machine learning model.