Hello everyone, welcome back to CybercityHelp. In our last article of Machine Learning, we already discussed the basics of data, features, and labels, and how they play an important role in training any machine learning model. So we will not explain everything again, if you’re new, then please read our previous article and then continue with this one. Here is the link of previous article: Machine Learning Data Basics: Features, Labels, Differences, Importance and Clear Examples
So in our today’s article, we are going to learn about the basics of Machine Learning Dataset such as: what a dataset is, what different types of datasets exist, what train-test split means, why validation data is used, and how all these parts help in building accurate and reliable machine learning models. So let’s get started.
What Is Dataset in Machine Learning?
A dataset in Machine Learning is a structured collection of data that we use to train and evaluate a machine learning model. It contains many examples, and each example is usually made up of features as input and sometimes a label as output. The model studies this dataset to learn patterns and relationships so that it can make predictions on new and unseen data.
For example, if you want to build a model to predict house prices, your dataset will include many records such as the size of the house, number of rooms, location, age of the house, and the actual selling price. All these records together form a dataset, and the machine learns from this collection of examples.
Types of Datasets in Machine Learning
In Machine Learning, datasets can be categorized in different ways depending on how the data is labeled, how it is used during the learning process, how the classes are distributed, and how the data is structured.
For example, below are the main ways in which generally datasets are being categorized in Machine Learning:
1. Based on Label Availability
Labeled Dataset
A labeled dataset is a dataset where each data point is already tagged with the correct output or answer. This means the model knows both the input and the expected output during training.
For example, in an email spam detection system, the dataset contains emails along with a label like “spam” or “not spam.” The model learns by comparing its predictions with these known labels.
Unlabeled Dataset
An unlabeled dataset contains only input data without any correct output labels. The model does not know the answers in advance and must find hidden patterns or structures by itself.
For example, if you have customer purchase data without any category or tag, the model may group similar customers together based on behavior.
2. Based on Usage in the Machine Learning Process
Training Dataset
The training dataset is the portion of data used to teach the machine learning model. The model learns patterns, relationships, and rules from this data.
For example, if you have 10,000 images of handwritten digits, most of them are used for training so the model can learn how different digits look.
Validation Dataset
The validation dataset is used during the training process to tune model parameters and make decisions like selecting the best model or stopping training at the right time.
It acts as a middle step between training and testing and helps prevent the model from becoming too specialized on training data.
Testing Dataset
The testing dataset is used to evaluate the model after training is complete. The model has never seen this data before, so it helps measure how well the model can generalize to new, unseen data.
For example, after training a model to recognize faces, you test it on new images to check its accuracy.
3. Based on Class Distribution
Balanced Dataset
A balanced dataset is one where all classes have roughly the same number of data samples. For example, in a dataset for disease detection, a balanced dataset would have a similar number of healthy and diseased cases.
Imbalanced Dataset
An imbalanced dataset is one where some classes have many more samples than others. For example, fraud detection datasets often have very few fraud cases compared to normal transactions.
4. Based on Data Structure
Structured Dataset
Structured datasets are organized in a clear format like tables, rows, and columns. For example, an Excel sheet containing customer age, income, and purchase history is a structured dataset.
Unstructured Dataset
Unstructured datasets do not follow a fixed format and include data like text, images, audio, and videos. For example, social media posts, speech recordings, or CCTV footage are unstructured data.
Important Note:
Machine learning datasets are not classified in only one way. Instead, they are categorized based on different criteria such as labeling, usage, class distribution, and structure. This flexible categorization helps in selecting the right algorithms and building reliable models.
What Is Training and Testing Data Split in Machine Learning?
In Machine Learning, we do not use the entire dataset to train a model at once. Instead, we divide the available data into two main parts called the training data and the testing data. This process is known as the training and testing data split.
The main purpose of this split is to make sure that the model does not just memorize the data, but actually learns patterns that can work well on new, unseen data.
What is Training Data?
The training data is the portion of the dataset that is used to teach the machine learning model. The model looks at this data and learns relationships between input features and output labels.
For example, if you are building a model to predict house prices, the training data will include past house details like size, location, number of rooms, and their corresponding prices. The model learns from these examples. Usually, around 70% to 80% of the dataset is used for training.
What is Testing Data?
The testing data is the remaining portion of the dataset that is kept separate and not shown to the model during training. It is used only after the training process is complete.
The purpose of testing data is to check how well the model performs on completely new data. This helps us understand the real accuracy and reliability of the model. Usually, around 20% to 30% of the dataset is used for testing.
Why Is the Split Necessary?
If we evaluate a model using the same data it was trained on, the accuracy may look very high, but this does not mean the model will perform well in real life. This is called overfitting.
By using a separate testing dataset, we get a realistic measure of how the model will behave on unseen data and whether it has truly learned useful patterns.
For Example:
Suppose you have a dataset of 1,000 customer records. Where 800 records are used as training data and 200 records are used as testing data. So the model learns from the 800 records and is evaluated on the 200 new records to check its performance.
What Is Validation Data in Machine Learning?
In Machine Learning, validation data is a separate portion of the dataset that is used during the training process to evaluate and improve the model before final testing.
It acts as a middle checkpoint between training and testing. The model does not learn from the validation data, but it uses it to adjust its settings and make better decisions while training.
Alright, so this was the complete explanation of Machine Learning Dataset Basics in the easiest language possible. We discussed what a dataset is, different types of datasets, what training, testing, and validation data mean, why data splitting is important, and how all these parts help in building accurate and reliable machine learning models.
We hope that this article was useful for you and helped you clearly understand the concept of datasets in Machine Learning. In case if you are still unsure about any part of this topic or want more examples, then you can freely ask your doubts in the comment section. We will try to answer your questions as soon as possible. So stay connected, and that’s all for today’s article. Thank you so much for reading this article till the end!
“So Keep learning, Keep growing!”
