ROC Curve and AUC: Decode Model Ranking in 4 Steps
The ROC curve plots true positive rate vs false positive rate across all thresholds; AUC summarises it as one number. Learn how to read them, what a good AUC is, and ROC-AUC vs PR-AUC.
F1 Score: 1 Balanced Number for Precision and Recall
The F1 score is the harmonic mean of precision and recall: F1 = 2·(P·R)/(P+R). Learn why it uses a harmonic mean, when to use it, F-beta, and macro vs micro vs weighted F1.
Precision and Recall: The 2 Metrics Accuracy Hides
Precision is TP/(TP+FP) and recall is TP/(TP+FN). Learn the difference, the precision–recall trade-off, and which to prioritise for imbalanced problems like fraud, disease and spam.
Confusion Matrix: Read It Right in 4 Simple Steps
A confusion matrix compares predicted vs actual labels across four cells — TP, FP, TN, FN — and every classification metric (accuracy, precision, recall, F1) is derived from them.
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Multiplying Vectors: 5 Essential Methods You Need to Know
Master Multiplying Vectors: 5 Essential Methods You Need to Know. Scalar, dot, cross, component-wise, and outer products with worked examples. Understand magnitude connections.
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Essential Unit Vectors: 7 Key Concepts for Machine Learning
Master Unit Vectors: 7 Key Concepts for Machine Learning with clear explanations of normalization, cosine similarity, gradient descent direction, and orthonormal bases.
Eigenvalue Calculator
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Mean Median Mode Calculator
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Factors of 98
Find the factors of Find factors — Prime factorization— Number of factors— Sum of factors— Factor pairs— Is it prime?—
Factors of 96
Find the factors of Find factors — Prime factorization— Number of factors— Sum of factors— Factor pairs— Is it prime?—