FabriXAI User Guide

# Machine Learning Assistant

The Machine Learning Assistant is an AI-driven tool that provides comprehensive insights into the development of machine learning models. It covers essential topics such as data preparation, feature engineering, model selection, training, evaluation, overfitting management, performance interpretation, and ethical considerations. Whether you're a beginner or an experienced machine learning engineer, this assistant will guide you through the necessary steps to create effective and responsible machine learning solutions.

# Getting Started

To get started, make sure you have cloned the Machine Learning Assistant. Follow Create from Template to clone and configure your AI agent.

# Configuration Instructions

# Knowledge

You can upload .pdf or .txt files to provide the agent with tailored context that meets your specific requirements.

# User Inputs

No input fields are required for these AI agents.

# AI Models

The default model is Llama 3.1 8B Instruct, but you can explore other supported AI models as well. If needed, refine your prompts for more tailored outputs.

TIP

For more detailed instructions on configuring an AI agent, please refer to Configure AI Agent

# Sample Inputs and Output

# Sample Inputs

Can you explain the steps involved in data preparation for machine learning?

# Sample Output

Data preparation involves several key steps:
1. Data Collection: Gather data from various sources such as databases, APIs, or web scraping.
2. Data Cleaning: Identify and handle missing values, remove duplicates, and correct inconsistencies.
3. Data Preprocessing: Normalize or standardize the data, convert categorical variables into numerical formats, and split the data into training and testing sets.

# Tips of Effective Configuration

  • Define Clear Objectives: Clearly outline what you want to achieve with the machine learning model to guide the development process.
  • Utilize Relevant Data: Ensure that the data you provide is relevant and representative of the problem you are solving.
  • Iterate and Experiment: Don't hesitate to iterate on your model and experiment with different configurations to find the best performance.
  • Monitor Ethical Implications: Always consider the ethical implications of your model, including potential biases in the data.
  • Seek Feedback: Engage with peers or mentors to get feedback on your model and its results for continuous improvement.

Continue exploring our collection of Agent Templates to discover more AI agent ideas in action!