# Technical Data Extraction: Accuracy vs. Temperature across Standard Deviations
This document contains a detailed extraction of data from a series of four line charts. The charts illustrate the relationship between **Temperature** (x-axis) and **Accuracy** (y-axis) under four different standard deviation (**std**) conditions.
## 1. Global Chart Specifications
* **Layout:** Four subplots arranged horizontally.
* **X-Axis Label:** "Temperature" (Common to all subplots).
* **X-Axis Scale:** Linear, ranging from 0.0 to 0.5 with major ticks at intervals of 0.1.
* **Y-Axis Label:** "Accuracy" (Common to all subplots).
* **Y-Axis Scale:** Linear, ranging from 0.0 to 1.0 with major ticks at intervals of 0.2.
* **Data Series 1 (Solid Pink Line):** Represents the model's performance.
* **Data Series 2 (Dashed Black Line):** Labeled "Best Expert". This represents a constant baseline performance for each specific standard deviation.
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## 2. Subplot Analysis
### Subplot 1: std: 0.1
* **Baseline (Best Expert):** Constant at approximately **0.92**.
* **Model Trend:** The pink line starts slightly below the expert, remains stable until Temperature 0.1, then exhibits a sharp, accelerating decline.
* **Data Points (Approximate):**
* Temp 0.0: 0.91
* Temp 0.1: 0.91
* Temp 0.2: 0.86
* Temp 0.3: 0.65
* Temp 0.4: 0.35
* Temp 0.5: 0.16
### Subplot 2: std: 0.2
* **Baseline (Best Expert):** Constant at approximately **0.83**.
* **Model Trend:** The pink line starts above the expert baseline. It remains flat until Temp 0.1, then crosses below the expert line at approximately Temp 0.18, continuing a steep decline.
* **Data Points (Approximate):**
* Temp 0.0: 0.86
* Temp 0.1: 0.86
* Temp 0.2: 0.81
* Temp 0.3: 0.62
* Temp 0.4: 0.33
* Temp 0.5: 0.16
### Subplot 3: std: 0.4
* **Baseline (Best Expert):** Constant at approximately **0.72**.
* **Model Trend:** The pink line starts significantly above the expert baseline. It remains flat until Temp 0.1, then crosses below the expert line at approximately Temp 0.22, followed by a steep decline.
* **Data Points (Approximate):**
* Temp 0.0: 0.82
* Temp 0.1: 0.82
* Temp 0.2: 0.78
* Temp 0.3: 0.59
* Temp 0.4: 0.32
* Temp 0.5: 0.15
### Subplot 4: std: 0.6
* **Baseline (Best Expert):** Constant at approximately **0.61**.
* **Model Trend:** The pink line starts well above the expert baseline. It remains flat until Temp 0.1, then crosses below the expert line at approximately Temp 0.28, followed by a steep decline.
* **Data Points (Approximate):**
* Temp 0.0: 0.75
* Temp 0.1: 0.75
* Temp 0.2: 0.71
* Temp 0.3: 0.53
* Temp 0.4: 0.30
* Temp 0.5: 0.15
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## 3. Comparative Summary and Key Findings
| Standard Deviation (std) | Best Expert Accuracy | Model Initial Accuracy (Temp 0.0) | Temp at which Model falls below Expert |
| :--- | :--- | :--- | :--- |
| **0.1** | ~0.92 | ~0.91 | Immediately (< 0.0) |
| **0.2** | ~0.83 | ~0.86 | ~0.18 |
| **0.4** | ~0.72 | ~0.82 | ~0.22 |
| **0.6** | ~0.61 | ~0.75 | ~0.28 |
### Key Observations:
1. **Inverse Relationship with Temperature:** In all scenarios, increasing Temperature beyond 0.1 leads to a significant and rapid decrease in model Accuracy.
2. **Impact of Noise (std):** As the standard deviation increases, the "Best Expert" baseline accuracy drops significantly (from ~0.92 to ~0.61).
3. **Model Robustness:** Interestingly, as the standard deviation increases, the model is able to maintain performance above the "Best Expert" for a wider range of Temperature values (the crossover point moves from <0.0 to ~0.28).
4. **Convergence:** Regardless of the starting accuracy or standard deviation, all models converge to a very low accuracy (between 0.15 and 0.16) when the Temperature reaches 0.5.
