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--- |
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license: ecl-2.0 |
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task_categories: |
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- tabular-classification |
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language: |
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- en |
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tags: |
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- public |
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- tabular |
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- signal |
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- education |
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pretty_name: Sonar Rock Mine |
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size_categories: |
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- n<1K |
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--- |
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The **Sonar Rock Mine** is a well-known benchmark dataset in the field of machine learning and pattern recognition, originally collected for the purpose of distinguishing between **mines (M)** and **rocks (R)** using sonar signals. |
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### Dataset Overview: |
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- **File**: `raw_sonar.csv` |
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- **Number of Features**: 60 continuous numerical values per instance |
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- **Target Label**: One class label at the end of each row, either: |
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- `M` → Mine (target object: simulated sea mine) |
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- `R` → Rock (non-mine object) |
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- **Total Instances**: The dataset contains multiple rows (exact count depends on full file), each representing a single sonar signal return. |
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### Feature Description: |
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Each of the 60 features represents the **energy** or **reflected signal strength** recorded across different frequency bands at successive time intervals. These values are the result of bouncing sonar signals off underwater objects. |
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- The values are **normalized** and typically range between **0.0 and 1.0**. |
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- They capture the **acoustic signature** of an object based on how it reflects sonar pulses. |
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- The variation in these reflections helps differentiate between the smooth, hard surface of a rock and the more complex, resonant structure of a mine. |
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### Class Distribution: |
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The dataset generally has a relatively balanced distribution between the two classes: |
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- **Mine (M)**: Signals reflected from simulated mines (often cylindrical, metal objects). |
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- **Rock (R)**: Signals from natural rock-like objects or geological formations. |
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> In real-world applications, this imbalance may vary, but the standard version used in ML is fairly balanced. |
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### Objective: |
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The primary goal with this dataset is to build a **binary classification model** that can accurately classify whether a given sonar signal corresponds to a **mine** or a **rock**, which is critical for naval safety and underwater navigation. |
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### Key Characteristics: |
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| Property | Description | |
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|--------|-------------| |
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| Task Type | Binary Classification | |
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| Input Type | Continuous numerical features (time-series-like signal returns) | |
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| Number of Features | 60 | |
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| Number of Classes | 2 (`M` = Mine, `R` = Rock) | |
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| Typical Use Cases | Classification algorithms, feature selection, noise analysis, neural networks, SVM, decision trees | |
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| Challenges | High similarity between some signals, potential overfitting due to high dimensionality relative to sample size | |
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### Summary: |
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This dataset provides a realistic example of **sensory data classification**, where subtle differences in signal patterns must be detected to make accurate decisions. It's widely used for teaching and evaluating classification techniques, especially in scenarios involving **signal processing** and **underwater object detection**. |
