LLM response Plot data-frame #33
Description
Feature: A new plot data frame that can be used directly on your notebook
Overview: This update introduces a new utility function, plot_ml_metrics, designed to automatically generate horizontal bar charts from JSON output data, displaying key machine learning metrics such as accuracy, precision, recall, and F1-score.
Purpose: The function aims to streamline the visualization of machine learning model performance, providing an intuitive and reusable plotting tool. This feature will be particularly useful for those who want to quickly assess the effectiveness of their models without manually configuring plots each time.
Key Features:
Automatic JSON Parsing: The function accepts JSON-formatted data and extracts relevant metrics for plotting.
Reusable & Notebook-Friendly: Designed for easy integration into Jupyter notebooks, allowing users to generate plots with minimal code.
Customizable & Extensible: Users can adapt the function to handle different types of metrics or datasets, making it a versatile tool for various projects.
Usage:
This function will automatically generate a clear, organized bar chart based on the llm report to visually represent the model's performance.
Next Steps: I'm also exploring options to enhance the readability and presentation of the output logs from LogLLM. Once I finalize the improvements, I'll share my recommendations for integration.