Overview
Welcome to the documentation for the HRClassifier library. This library provides an easy-to-use tool for text classification using machine learning algorithms. Whether you’re working on sentiment analysis, document categorization, or any other text classification task, HRClassifier simplifies the process and gives you accurate results.
Installation
To get started with HRClassifier, you need to follow these steps:
Step 1: Prerequisites
Before installing HRClassifier, make sure you have the following prerequisites:
- Python (version X.X.X or later)
- pip package manager (included with Python)
Step 2: Install HRClassifier
To install HRClassifier, use the following command:
pip install hrlclassifier
Usage
Once installed, you can start using HRClassifier in your Python projects. The following example demonstrates the basic usage:
import hrlclassifier
# Create an instance of HRClassifier
classifier = hrlclassifier.HRClassifier()
# Train the classifier
classifier.train(X_train, y_train)
# Classify new text
result = classifier.classify(new_text)
# Print the predicted class
print(result)
Training Data
Before classifying any text, you need to train the classifier with labeled data. The training data should be in the form of lists X_train
and y_train
, where X_train
represents the input texts and y_train
represents their corresponding labels.
Classifying New Text
To classify new text using HRClassifier, you can use the classify
method. Simply pass the text you want to classify as the argument, and the method will return the predicted class.
Example:
result = classifier.classify("This is an example text to classify.")
print(result) # Output: "positive"
API Reference
HRClassifier provides a set of intuitive methods to simplify the text classification process. The following is a brief overview of the available methods:
train
train(X_train, y_train)
trains the HRClassifier with labeled training data. It takes two arguments, X_train
and y_train
, representing the input texts and their corresponding labels, respectively.
Arguments:
X_train
: list of input texts.y_train
: list of corresponding labels.
classify
classify(text)
classifies a new text and returns the predicted class.
Arguments:
text
: the text to classify.
Conclusion
Congratulations! You’ve learned the basics of using HRClassifier for text classification. With its powerful functionality and simple usage, you’re now equipped to handle a wide range of text classification tasks.