Unlabelled datasets are collections of data that lack explicit annotations or labels that identify or classify the data points in a meaningful way. In contrast to labelled datasets, where each entry is tagged with a label or outcome (such as “spam” or “not spam” for emails), unlabelled datasets simply contain the raw data without any guiding information regarding its categorization or the outcome that each data point is associated with. So, if you want to learn how to label unlabelled datasets, this article is for you. In this article, I will help you understand what is unlabelled data and how to label such datasets.
Identifying Unlabelled Datasets
Unlabelled datasets are data points without labels. For example, the table below is an example of a labelled dataset:
A labelled data is a collection of features and labels. Features represent the columns that are independent of each other, and labels represent the columns that are dependent on the change in values of the independent features. When we work on a classification problem, we need to have a column representing the labels so that we can find relationships between features and labels to predict labels on unseen/future data.
Now, here’s an example of an unlabelled dataset:
We can use labelled data to train models to find relationships between features and labels. However, a dataset without labels does not have a target variable to find relationships.
How to Label Unlabelled Datasets?
Labelling unlabelled datasets can be approached in various ways depending on the type of data, the desired labels, and the resources available. Let’s go through some methods that are used in the real world to label an unlabelled dataset.
Semi-Supervised Learning
This approach leverages a small set of manually labelled data alongside a large volume of unlabelled data. Algorithms are trained on the manually labelled set and then applied to predict labels for the unlabelled set, which often involves iterative processes to refine the model’s accuracy.
For example, let’s consider a sentiment analysis problem where we aim to classify customer reviews into:
- positive sentiments
- negative sentiments
- or neutral sentiments
First, we will manually label a small subset of the customer reviews. For instance, we might label 1000 reviews out of a dataset containing 100,000 reviews. Then, we will use the manually labelled dataset to train an initial sentiment analysis model. This model will learn to predict sentiment based on the features extracted from the text of the reviews.
Next, we will use this model, trained on manually labelled data, to predict sentiments for the large set of unlabelled reviews.
Unsupervised Learning
Techniques like clustering can group similar data points together based on their features. These groups can then be analyzed to assign labels.
Let’s say an e-commerce company wants to segment its customers to tailor marketing strategies according to different customer behaviours. The dataset contains customer transaction data but doesn’t contain information about which segment a customer falls into.
To label such a dataset, we will start by selecting relevant features that could indicate customer purchasing behaviour patterns. Then, we will apply a clustering algorithm, like K-means, to group customers based on the selected features. The algorithm will identify clusters of customers with similar purchasing behaviours without prior labels.
After clustering, we will analyze the characteristics of each cluster to understand the common behaviours within each group. Based on this analysis, we can assign meaningful labels to each cluster, such as:
Summary
So, this is how you can label your unlabelled datasets. Here are some clustering problems that will help you learn more about labelling a dataset with practical implementation using Python:
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