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SLP Algorithm Training Data
Data Science and Analytics
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About
A focused dataset designed for implementing and modifying Single Layer Perceptron (SLP) algorithms. This small, tabular dataset is structured as a simple NumPy array, making it an excellent resource for beginners or those validating machine learning models. It features 13 distinct samples, each defined by three input features and a binary class label (0 or 1). The structure includes a mandated Feature 1 value of 1 for every sample, aligning with the common SLP algorithm requirement where the initial input ($x_0$) is set to 1.
Columns
- Pattern: An integer variable used for tracking samples. It has 13 valid values across 14 total observations.
- Feature1 (Feature No.01): The first feature input, consistently set to 1. It serves as the required initial input ($x_0$) for the perceptron calculation.
- Feature2 (Feature No.02): A numerical feature input, with values ranging from 0.08 (minimum) to 0.92 (maximum). The mean value is approximately 0.44.
- Feature3 (Feature No.03): The third numerical feature input, with values ranging from 0.1 (minimum) to 1 (maximum). The mean value is approximately 0.58.
- Class_Label (Binary Class(0/1)): The target label for classification, defined strictly as binary (0 or 1).
Distribution
The dataset is provided in CSV format and is notably small, weighing approximately 110.26 kB. It contains 5 columns in total. Out of 141 total observations per column, 13 are valid, indicating a minor 7% missing rate (1 sample) across all data points. The dataset is explicitly partitioned for model development, containing eight samples designated for training and five samples reserved for validation purposes.
Usage
This data product is perfectly suited for testing and modifying SLP machine learning algorithms. Ideal applications include:
- Algorithmic Validation: Testing custom implementations of the Single Layer Perceptron model.
- Educational Purposes: Serving as introductory material for students learning about neural network fundamentals, particularly supervised classification.
- Code Debugging: A small, predictable dataset ideal for ensuring machine learning code (especially in environments like NumPy) provides correct outputs.
Coverage
This is an abstract dataset generated specifically for computational purposes. It has no geographic, time range, or demographic scope. The focus is purely on providing a controlled, small sample space for implementing and testing basic artificial neural networks.
License
CC0: Public Domain
Who Can Use It
- Beginner Programmers and Students: For learning fundamental Deep Learning concepts and basic pattern recognition using Python libraries like NumPy and Matplotlib.
- Computer Science Educators: As a reliable, small example for assignments and classroom demonstrations related to early neural network architecture.
- Algorithm Developers: For quick testing and iteration cycles when modifying or creating new Single Layer Perceptron variants.
Dataset Name Suggestions
- SLP Algorithm Training Data
- Perceptron Binary Classification Sample
- Small Neural Network Test Data
- NumPy Perceptron Example
Attributes
Original Data Source: SLP Algorithm Training Data
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