LuminaAI/Pima_Indians-Tabular

Pima Indians Tabular Data RCL Dataset

Overview

This dataset contains tabular data structured explicitly for classification tasks using Lumina AI's Random Contrast Learning (RCL) algorithm via the PrismRCL application. Unlike textual or imaging datasets, tabular datasets contain numeric or categorical data organized in individual .txt files with space-separated values.

Dataset Structure

The dataset structure for tabular classification training:

pima-indians_data/
    train/
        [class_1]/
            sample_001.txt
            sample_002.txt
            ...
        [class_2]/
            sample_001.txt
            sample_002.txt
            ...
    test/
        [class_1]/
            sample_001.txt
            sample_002.txt
            ...
        [class_2]/
            sample_001.txt
            sample_002.txt
            ...

• Classes: Folder names represent distinct data classes.
• Tabular Samples: Each .txt file represents a single data sample with features as space-separated values.

Tabular Data Preparation

For tabular datasets, PrismRCL has specific preparation requirements:

• Data samples must be in .txt format.
• Each file should contain a single line with space-separated features.
• No normalization of numerical values is required when using PrismRCL version 2.4.0 or later.
• File names must be unique across all class folders.

Usage (Tabular-specific)

Use PrismRCL for training with tabular data:

C:\PrismRCL\PrismRCL.exe naivebayes rclticks=10 ^
  data=C:\path\to\pima-indians_data\train testdata=C:\path\to\pima-indians_data\test ^
  savemodel=C:\path\to\models\pima_indians_model.classify ^
  log=C:\path\to\log_files stopwhendone

Explanation of Command

• naivebayes: Specifies Naive Bayes as the evaluation method for tabular classification.
• rclticks: Number of RCL iterations during training.
• data & testdata: Paths to training and testing tabular datasets.
• savemodel: Output path for the trained classification model.
• log: Directory for storing log files.
• stopwhendone: Automatically terminates the session after training completion.

Auto Optimize

PrismRCL includes an Auto Optimize feature designed to automatically identify optimal training parameters for your specific dataset, significantly streamlining the model training process. This feature removes the need for manual parameter tuning by systematically evaluating your data to determine the most effective settings for evaluation method, rclticks, boxdown, and other relevant parameters.

How to Use Auto Optimize:

Run the following command with your dataset:

C:\PrismRCL\PrismRCL.exe auto-optimize data=C:\path\to\your_dataset\train log=C:\path\to\log_files

Explanation:

• auto-optimize: Initiates PrismRCL’s parameter optimization process.
• data: Path to your training dataset.
• log: Specifies the directory where PrismRCL will save a detailed summary file with optimal parameters determined by the optimization process.

After execution, PrismRCL generates an optimization summary file in your specified log directory (_optimize_summary_mm_dd_yy_hh_mm_ss.txt). This file will list the optimal parameters, which you should then apply in your training commands to achieve optimal model performance.

License

This dataset is licensed under the MIT License.

Original Source

Prepared explicitly by Lumina AI for RCL-based tabular data classification training. Please credit Lumina AI when using this dataset in research or applications.

Additional Information

Refer to the PrismRCL Technical Documentation v2.6.2 for more detailed guidance on tabular data preparation and parameter specifications.