What does a confusion matrix show?
Quality Thought – The Best Data Science Training in Hyderabad
Looking for the best Data Science training in Hyderabad? Quality Thought offers industry-focused Data Science training designed to help professionals and freshers master machine learning, AI, big data analytics, and data visualization. Our expert-led course provides hands-on training with real-world projects, ensuring you gain in-depth knowledge of Python, R, SQL, statistics, and advanced analytics techniques.
Why Choose Quality Thought for Data Science Training?
✅ Expert Trainers with real-time industry experience
✅ Hands-on Training with live projects and case studies
✅ Comprehensive Curriculum covering Python, ML, Deep Learning, and AI
✅ 100% Placement Assistance with top IT companies
✅ Flexible Learning – Classroom & Online Training
Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in how they process and learn from data.
Neural networks are a type of machine learning model inspired by the structure and function of the human brain. They are designed to recognize patterns and relationships in data through a process of learning.
A confusion matrix is a table used to evaluate the performance of a classification model by showing the actual vs predicted classifications.
What it shows:
| Predicted Positive | Predicted Negative | |
|---|---|---|
| Actual Positive | True Positive (TP) | False Negative (FN) |
| Actual Negative | False Positive (FP) | True Negative (TN) |
-
True Negative (TN): Correctly predicted negative cases.
-
False Positive (FP): Incorrectly predicted positive (Type I error).
-
False Negative (FN): Incorrectly predicted negative (Type II error).
Why it’s useful:
-
Helps calculate metrics like accuracy, precision, recall, F1-score.
-
Identifies types of errors your model makes.
-
Useful for imbalanced datasets where accuracy alone is misleading.
Summary:
A confusion matrix provides a detailed breakdown of classification results, showing where the model got predictions right or wrong, which helps in understanding and improving model performance.
Read More
Comments
Post a Comment