# Difference Between Accuracy and Precision

Accuracy and precision are two crucial concepts in measurement and data analysis. Accuracy refers to how close a measured value is to the true or accepted value, indicating the degree of correctness in a set of measurements. On the other hand, precision measures the consistency or reproducibility of repeated measurements. A precise measurement yields similar results in multiple trials, even if they are not necessarily close to the true value.

## What is Accuracy?

The quality or state of being perfect and precise is called accuracy. The extent to which a measure, calculation, or specification’s result complies with the intended result or a standard.

The degree to which a collection of measures (observations or readings) corresponds to their actual value is known as accuracy. The degree to which something is accurate relates to how close it is to its genuine worth.

Accuracy is a type of observational error. High accuracy requires precision and high trueness. Commonly accuracy is a systematic error, a measurement of statistical bias, and a measure of central tendency.

Low accuracy leads to a difference between a result and the true value. In science and engineering, accuracy is a measurement of the degree of closeness and measurement of quantity; however, accuracy is a strategic goal you land directly on your target value.

Low accuracy means you are not achieving your target. Accuracy is determined after a single even though repeatability is critical in determining long-term success.

For example, you set a KPI to decrease the site bounce rate in the upcoming year by 12%. If you achieve the estimated value, your accuracy is 100%.

### The Core Formula

At its core, accuracy is calculated as the quotient of true positives and true negatives divided by the total number of instances. The formula for accuracy is deceptively simple yet profoundly insightful:

Accuracy = (True Positives + True Negatives) / Total Instances

### True Positives and True Negatives

To comprehend accuracy fully, one must delve into the components that constitute it. True positives denote instances where the model correctly predicts the positive class, while true negatives represent instances where the model accurately identifies the negative class. Both are the building blocks of accuracy, reinforcing our understanding of the model’s ability to discern and classify.

### The Pitfalls: False Positives and False Negatives

However, the accuracy story would be incomplete without acknowledging its flip side. False positives and false negatives inject a dose of realism into our assessments. False positives occur when the model incorrectly predicts the positive class, while false negatives emerge when the model fails to identify instances belonging to the positive class. These missteps, though inevitable, underscore the limitations of accuracy, especially in imbalanced datasets.

### The Balance Challenge

Accuracy shines brightest in balanced datasets, where the classes are evenly distributed. Yet, its radiance dims when faced with imbalances. In scenarios where one class dominates, a high accuracy score may be misleading. This is where precision, recall, and F1 score step in, providing a more nuanced evaluation, especially when the stakes are high in fields like healthcare or fraud detection.

### The Human Element

Beyond the mathematical intricacies, accuracy embodies the human pursuit of perfection in predictions. It serves as a compass, guiding data scientists and machine learning practitioners through the labyrinth of model evaluation. Its simplicity conceals a profound depth, urging us to balance the scales of correct predictions and navigate the intricate terrain of true and false. In the tapestry of machine learning metrics, accuracy remains a cornerstone, weaving together the threads of success and failure.

### Examples of Accuracy

1. Dart Thrower: Imagine you’re playing darts, and your target is the bullseye. If most of your throws hit the bullseye, you’re accurate. Your accuracy is high because you’re consistently getting close to the desired point.
2. GPS Navigation: When you use a GPS device to navigate, you want it to accurately pinpoint your location and guide you to your destination. If it consistently guides you to the correct places, it’s accurate in its directions.
3. Archery: In archery, hitting the center of the target consistently demonstrates accuracy. If an archer’s arrows cluster near the bullseye, it reflects a high level of accuracy in their aim.
4. Medical Thermometer: A good medical thermometer should provide an accurate reading of body temperature. If it consistently gives the correct temperature, it is accurate in its measurements.
5. Weather Forecasting: Meteorologists aim for accuracy when predicting weather conditions. If their forecasts align closely with the actual weather events, it indicates a high level of accuracy in their predictions.

## What is Precision?

It is the measurement of how close your results are. Precision is measured with time. It needs repeatability to determine the level of proximity among each set of measurements.

If your results are similar, it is high precision; on the other hand, low precision is when your results are not similar. Measurement of precision is done in two scenarios:

• when you try to keep away from making the same mistake.
• when you are achieving victorious results and need to establish reliability.

For example, all of your pages of websites have a high bounce rate, and the same percentage decreases these. So each site page has the same number of reduced bounce rates, 6% or 20%. It is precise but not accurate.

### Definition and Calculation

Precision is formally defined as the ratio of true positive predictions to the total number of positive predictions made by a model. Mathematically, it is expressed as:

Precision = True Positives / (True Positives + False Positives)

In simpler terms, precision answers the question: “Of all the instances predicted as positive, how many were actually positive?”

### Context in Classification

In the context of binary classification, precision becomes especially relevant when the cost of false positives is high. Consider a medical diagnosis scenario where the positive class represents a severe condition. High precision ensures that when the model predicts a positive case, it is highly likely to be accurate, minimizing the chances of misdiagnosis.

### Trade-off with Recall

Precision operates in tandem with another metric called recall. While precision focuses on minimizing false positives, recall emphasizes minimizing false negatives. Striking a balance between precision and recall is crucial, as an increase in one may lead to a decrease in the other. This trade-off is visualized through the precision-recall curve.

### Implications in Real-world Applications

Precision’s significance extends beyond the realm of machine learning. In fields like finance, where fraud detection is paramount, a high precision model ensures that flagged transactions as fraudulent are more likely to be genuine, reducing the risk of blocking legitimate transactions.

### Interpretation and Limitations

A precision score of 1.0 indicates a perfect model with no false positives, while a score of 0.0 suggests the opposite. It’s important to note that precision might not be the sole determinant of model performance, and its interpretation should consider the specific objectives and consequences associated with false positives.

### Examples of Precision

1. Sharpshooter: Picture a sharpshooter hitting the same spot on a target repeatedly. Even if it’s not the bullseye, the tight grouping of shots indicates precision. The shots are consistently close to each other.
2. Chemistry Lab Pipetting: In a chemistry lab, precise measurements are crucial. If a scientist consistently dispenses the exact volume of liquid required, it demonstrates precision in their pipetting technique.
3. Watchmaker: Crafting a delicate watch requires precision. A skilled watchmaker can assemble tiny components with exacting accuracy, resulting in a timepiece that consistently keeps accurate time.
4. Jeweler’s Scale: Jewelers need precision when measuring gemstones. A precise scale ensures that each measurement is consistent and accurate, crucial when dealing with valuable materials.
5. Architectural Drafting: When an architect draws up plans, precision is vital. Accurate measurements and carefully aligned details ensure that the final construction matches the intended design precisely.

## Difference Between Accuracy and Precision

• Definition:
• Accuracy: Accuracy refers to the closeness of a measured value to a standard or known value. It indicates how well a measurement reflects the true or expected value.
• Precision: Precision, on the other hand, relates to the consistency or repeatability of measurements. It focuses on how close multiple measurements are to each other, regardless of their accuracy.
• Focus:
• Accuracy: The emphasis is on correctness, aiming for results that are as close as possible to the true value.
• Precision: The emphasis is on reliability and consistency, aiming for similar results when the same measurement is repeated.
• Measurement Error:
• Accuracy: Accuracy is influenced by both systematic errors (consistent inaccuracies) and random errors (fluctuations in measurements).
• Precision: Precision is primarily affected by random errors, as it gauges the variation between individual measurements.
• Graphical Representation:
• Accuracy: In a graphical representation, accuracy is reflected by how close data points are to the true or target value, depicted as a bullseye.
• Precision: Precision is illustrated by the clustering of data points, indicating how tightly grouped they are.
• Example:
• Accuracy: Imagine shooting arrows at a target – accuracy is hitting the center of the target.
• Precision: In the same archery analogy, precision is hitting closely grouped arrows, regardless of whether they hit the bullseye.
• Formula:
• Accuracy: Accuracy = (Number of Correct Measurements / Total Number of Measurements) * 100%
• Precision: Precision = (Number of Correctly Reproduced Measurements / Total Number of Repetitions) * 100%