Artificial Intelligence is as good as trained data. With the quantity & quality of training data directly determining the success of an AI algorithm, it is not surprising that an average of 80% of the time spent on an AI project is wrangling training data which includes data labeling.
Data labeling in the context of machine learning is the process of detecting as well as tagging data samples and it is crucial when it comes to supervised learning in ML. Supervised learning occurs when both data inputs and outputs are labeled to enrich future learnings of an AI model.
The complete data labeling workflow includes primarily data annotations, tagging, moderation, classification, and processing. So, you’ll need a comprehensive process to convert labeled data into the necessary training data to teach your AI models which recognize the patterns for producing the desired outcome.
For instance, training data for a facial recognition model might require tagging images with particular facial features like mouth, eyes, or nose.
So, Let’s Dive in and Learn How to Label Data in Python…
In machine learning, we deal with several kinds of datasets that contain multiple labels in one or more columns. These labels are in word or number forms. To make it readable by humans, these training data are labeled in words.
Therefore, Label Encoding refers to converting the labels into numeric forms and later converts them into machine-readable forms. Machine learning algorithms could decide how to operate those labels. It is a significant pre-processing step for structured datasets in supervised learning.
Label Encoder performs the conversion of predefined labels of categorical data into a numeric format.
For instance, when a dataset contains a variable called “Gender” with labels like “Male” and “Female”, then the label encoder would convert these labels into a numeric format and the outcome would be [0,1].
Hence, by converting those labels into integer format, the machine learning model would have a better understanding of operating datasets.
How to get started with Label Encoding? – the Syntax you should know
Python sklearn library offers you a predefined function for carrying out Label Encoding on any dataset.
Now, let’s create an object of the LabelEncoder class and then utilize it for applying label encoding on the data.
Label Encoding with sklearn
The first and foremost step to encode a dataset is to have a dataset. So, let’s create a simple dataset here…
So, we have created a ‘data’ dictionary and then transformed it into a DataFrame utilizing pandas.DataFrame( ) function.
Now, from the dataset, it is crystal clear that the variable “Gender” has labels as ‘F’ & ‘M’.
Next step is to import the LabelEncoder class and apply it on the ‘Gender’ variable of the dataset.
The fit_transform( ) method is used to apply the function of the label encoder pointed by the objects to the data variable.
So, you see obtaining high-quality labeled data is becoming challenging when more complex models are to be built.
But now, with the advancement of in data annotation, data labeling approaches don’t seem to be a distant dream.
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