---
license: ecl-2.0
task_categories:
- tabular-classification
language:
- en
tags:
- public
- tabular
- signal
- education
pretty_name: Sonar Rock Mine
size_categories:
- n<1K
---

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.

### Dataset Overview:
- **File**: `raw_sonar.csv`
- **Number of Features**: 60 continuous numerical values per instance
- **Target Label**: One class label at the end of each row, either:
  - `M` → Mine (target object: simulated sea mine)
  - `R` → Rock (non-mine object)
- **Total Instances**: The dataset contains multiple rows (exact count depends on full file), each representing a single sonar signal return.

### Feature Description:
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.

- The values are **normalized** and typically range between **0.0 and 1.0**.
- They capture the **acoustic signature** of an object based on how it reflects sonar pulses.
- The variation in these reflections helps differentiate between the smooth, hard surface of a rock and the more complex, resonant structure of a mine.

### Class Distribution:
The dataset generally has a relatively balanced distribution between the two classes:
- **Mine (M)**: Signals reflected from simulated mines (often cylindrical, metal objects).
- **Rock (R)**: Signals from natural rock-like objects or geological formations.

> In real-world applications, this imbalance may vary, but the standard version used in ML is fairly balanced.

### Objective:
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.

### Key Characteristics:
| Property | Description |
|--------|-------------|
| Task Type | Binary Classification |
| Input Type | Continuous numerical features (time-series-like signal returns) |
| Number of Features | 60 |
| Number of Classes | 2 (`M` = Mine, `R` = Rock) |
| Typical Use Cases | Classification algorithms, feature selection, noise analysis, neural networks, SVM, decision trees |
| Challenges | High similarity between some signals, potential overfitting due to high dimensionality relative to sample size |

### Summary:
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**.