high recall but too low precision result in imbalanced data
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## High Recall, Low Precision in Imbalanced Data: A Deep Dive with Code Examples
When dealing with imbalanced datasets in machine learning, achieving high recall at the expense of precision is a common scenario. This tutorial will explore the causes of this behavior, the implications, and practical techniques to address it using code examples in Python, primarily with libraries like Scikit-learn.
**1. Understanding Imbalanced Data**
Imbalanced data refers to a classification problem where the classes are not represented equally. One class, often called the *majority class* or *negative class*, significantly outnumbers the other, known as the *minority class* or *positive class*.
**Examples of Imbalanced Datasets:**
* **Fraud Detection:** Fraudulent transactions are rare compared to legitimate ones.
* **Medical Diagnosis:** Diseases are usually less prevalent than healthy individuals.
* **Spam Filtering:** Legitimate emails far outnumber spam emails.
* **Defect Detection in Manufacturing:** Defective products are less common than non-defective ones.
**Why is Imbalanced Data a Problem?**
Most machine learning algorithms are designed with the assumption of relatively balanced classes. When applied to imbalanced data, they tend to be biased towards the majority class. This bias stems from the optimization objective of minimizing overall error. The algorithm can achieve a high overall accuracy by simply predicting the majority class most of the time. However, the goal in many imbalanced classification tasks is to accurately identify the minority class, which is often the more important and actionable category.
**2. Recall vs. Precision: The Trade-off**
Before diving into the code, let's solidify our understanding of recall and precision:
* **Precision:** Out of all the instances predicted as positive, what proportion is actually positive? It measures the accuracy of the positive predictions.
* `Precision = True Positives / (True Positives + ...
#ImbalancedData
#HighRecallLowPrecision
#DataScience
Видео high recall but too low precision result in imbalanced data канала CodeSolve
## High Recall, Low Precision in Imbalanced Data: A Deep Dive with Code Examples
When dealing with imbalanced datasets in machine learning, achieving high recall at the expense of precision is a common scenario. This tutorial will explore the causes of this behavior, the implications, and practical techniques to address it using code examples in Python, primarily with libraries like Scikit-learn.
**1. Understanding Imbalanced Data**
Imbalanced data refers to a classification problem where the classes are not represented equally. One class, often called the *majority class* or *negative class*, significantly outnumbers the other, known as the *minority class* or *positive class*.
**Examples of Imbalanced Datasets:**
* **Fraud Detection:** Fraudulent transactions are rare compared to legitimate ones.
* **Medical Diagnosis:** Diseases are usually less prevalent than healthy individuals.
* **Spam Filtering:** Legitimate emails far outnumber spam emails.
* **Defect Detection in Manufacturing:** Defective products are less common than non-defective ones.
**Why is Imbalanced Data a Problem?**
Most machine learning algorithms are designed with the assumption of relatively balanced classes. When applied to imbalanced data, they tend to be biased towards the majority class. This bias stems from the optimization objective of minimizing overall error. The algorithm can achieve a high overall accuracy by simply predicting the majority class most of the time. However, the goal in many imbalanced classification tasks is to accurately identify the minority class, which is often the more important and actionable category.
**2. Recall vs. Precision: The Trade-off**
Before diving into the code, let's solidify our understanding of recall and precision:
* **Precision:** Out of all the instances predicted as positive, what proportion is actually positive? It measures the accuracy of the positive predictions.
* `Precision = True Positives / (True Positives + ...
#ImbalancedData
#HighRecallLowPrecision
#DataScience
Видео high recall but too low precision result in imbalanced data канала CodeSolve
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14 июня 2025 г. 19:03:04
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