Pearson correlation coefficient - Simple English Wikipedia, the free encyclopedia

Pearson's correlation is a mathematical formula used to calculate correlation coefficients between two datasets. Most computer programs have a command to calculate this such as CORREL(dataset x: dataset y). You can calculate this by...

Step 1: Find the mean of x, and the mean of y
Step 2: Subtract the mean of x from every x value (call them "a"), and subtract the mean of y from every y value (call them "b")
Step 3: Calculate: ab, a² and b² for every value
Step 4: Sum up ab, sum up a² and sum up b²
Step 5: Divide the sum of ab by the square root of [(sum of a²) × (sum of b²)]

Developed by Karl Pearson in the 1880's,

v t e Machine learning evaluation metrics
Regression	MSE MAE sMAPE MAPE MASE MSPE RMS RMSE/RMSD R² MDA MAD
Classification	F-score P4 Accuracy Precision Recall Kappa MCC AUC ROC Sensitivity and specificity Logarithmic Loss
Clustering	Silhouette Calinski-Harabasz Davies-Bouldin Dunn index Hopkins statistic Jaccard index Rand index Similarity measure SMC SimHash
Ranking	MRR DCG NDCG AP
Computer Vision	PSNR SSIM IoU
NLP	Perplexity BLEU
Deep Learning Related Metrics	Inception score FID
Recommender system	Coverage Intra-list Similarity
Similarity	Cosine similarity Euclidean distance Pearson correlation coefficient
Confusion matrix