Distance metrics (euclidean, cosine, manhattan) in SciPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the Euclidean distance between two points?

Euclidean distance is the straight-line distance between two points in space. It is calculated as the square root of the sum of squared differences between corresponding coordinates.

beginner

How does the Cosine distance measure similarity between two vectors?

Cosine distance measures the angle between two vectors. It calculates 1 minus the cosine of the angle, focusing on direction rather than magnitude.

beginner

What is the Manhattan distance and when is it useful?

Manhattan distance is the sum of absolute differences between coordinates of two points. It is useful in grid-like path problems, like city blocks.

intermediate

How to calculate Euclidean distance using scipy?

Use scipy.spatial.distance.euclidean(point1, point2) to get the Euclidean distance between two points.

intermediate

What is the difference between Euclidean and Manhattan distances?

Euclidean distance measures straight-line distance, while Manhattan distance sums absolute differences along each axis, like moving along grid lines.

Which distance metric measures the straight-line distance between two points?

A Cosine distance

B Euclidean distance

C Manhattan distance

D Hamming distance

Euclidean distance is the straight-line distance between two points.

Which distance metric focuses on the angle between two vectors?

A Cosine distance

B Euclidean distance

C Manhattan distance

D Jaccard distance

Cosine distance measures similarity based on the angle between vectors.

Manhattan distance is best described as:

A Sum of squared differences

B Difference of angles

C Minimum distance

D Sum of absolute differences

Manhattan distance sums the absolute differences of coordinates.

Which scipy function calculates Euclidean distance?

A scipy.spatial.distance.cosine()

B scipy.spatial.distance.manhattan()

C scipy.spatial.distance.euclidean()

D scipy.spatial.distance.jaccard()

scipy.spatial.distance.euclidean() calculates Euclidean distance.

If you want to measure distance like walking city blocks, which metric is best?

A Manhattan distance

B Cosine distance

C Hamming distance

D Euclidean distance

Manhattan distance sums absolute differences, like moving along grid paths.

Explain the differences between Euclidean, Cosine, and Manhattan distances with examples.

Think about how you measure distance walking straight, turning angles, or moving on a grid.

Describe how to use scipy to calculate Euclidean, Cosine, and Manhattan distances between two points.

Recall the scipy distance functions and their names.