Model Evaluation Metrics: The Complete Guide
Master every classification and regression metric from accuracy and F1 to AUC-ROC and confusion matrices, with Python code and guidance on when to use each.
·7 min read · #ml#metrics#evaluation
#classification
6 posts · page 1 of 1
K-Nearest Neighbors Algorithm Explained
Understand how the k-nearest neighbors algorithm classifies and regresses by looking at similar examples, when it works well, and how to tune k and distance metrics for real problems.
·4 min read · #machine learning#knn#classification
Naive Bayes Explained
A practical walkthrough of the Naive Bayes classifier: how it uses probability and a strong independence assumption to build a fast, surprisingly accurate baseline for text and tabular data.
·4 min read · #machine learning#naive bayes#classification
ML Precision Recall and F1 Explained
Decode precision, recall, F1, and accuracy with concrete intuition, threshold tuning, and PR vs ROC curve guidance for imbalanced data.
·5 min read · #ml#metrics#precision
ML SVM Explained With Intuition
An intuitive walkthrough of support vector machines, the kernel trick, and when SVMs still make sense in a world dominated by gradient boosted trees and neural networks.
·4 min read · #machine-learning#svm#classification
Logistic Regression for Binary Classification
Learn how logistic regression turns a linear score into a probability, how to train it with scikit-learn, and how to evaluate binary classifiers using ROC-AUC.
·5 min read · #machine-learning#classification#intermediate