### What is Jaccard Index / Distance?

The Jaccard Index measures similarity for two sets of data by computing the ratio of items present in both sets

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The Jaccard Index measures similarity for two sets of data by computing the ratio of items present in both sets

The Minkowski Distance is a general form for computing distances using an Lp norm.

The Manhattan distance, or L1 norm, measures the sum of absolute distance between two vectors.

The Mahalanobis Distance is a multivariate form of the Euclidean Distance that accounts for correlation between dimensions.

The Euclidean Distance, or L2 norm, is the most common distance metric used in clustering.

Some common distance metrics are: Euclidean distance, Mahalanobis distance, Manhattan distance, Minkowski distance, Cosine Similarity, Jaccard distance

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- Machine Learning 101 (30)
- Statistics 101 (38)
- Supervised Learning (114)
- Regression (42)
- Classification (46)
- Logistic Regression (10)
- Support Vector Machine (10)
- Naive Bayes (4)
- Discriminant Analysis (5)
- Classification Evaluations (9)

- Classification & Regression Trees (CART) (23)

- Unsupervised Learning (55)
- Clustering (28)
- Distance Measures (9)
- Dimensionality Reduction (9)

- Deep Learning (23)
- Data Preparation (34)
- General (5)
- Standardization (6)
- Missing data (7)
- Textual Data (16)