What is the synonym of sparse?

Some common synonyms of sparse are meager, scanty, scant, skimpy, and spare.

What does sparse mean mean?

of few and scattered elements
Definition of sparse

: of few and scattered elements especially : not thickly grown or settled.

What is sparse example?

The definition of sparse is a thinly spread or meager amount or supply. An example of sparse is when a vast forest has only a few little trees scattered about.

What are antonyms of sparse?

Antonyms, on the other hand, include full, lush, and plentiful. Definitions of sparse. adjective. not dense.

How do you use sparse?

(1) Many slopes are rock fields with sparse vegetation. (2) Vegetation becomes sparse higher up the mountains. (3) His pink scalp gleamed through his sparse hair. (4) The information available on the subject is sparse.

Is more sparse correct?

Either is fine with me. I would use “sparser” in a story or non-technical article, perhaps, and “more sparse” in a more technical context. This is simply a matter of taste.

What does sparse population mean?

small in numbers or amount, often spread over a large area: a sparse population/audience.

What is a sparse dataset?

Sparse data is a variable in which the cells do not contain actual data within data analysis. Sparse data is empty or has a zero value. Sparse data is different from missing data because sparse data shows up as empty or zero while missing data doesn’t show what some or any of the values are.

What does sparse environment mean?

adj scattered or scanty; not dense.

What does sparse hair mean?

Definition. Decreased number of hairs per unit area of skin of the scalp. [

What is sparse feature?

What are sparse features? Features with sparse data are features that have mostly zero values. This is different from features with missing data. Examples of sparse features include vectors of one-hot-encoded words or counts of categorical data.

What is sparse and dense data?

In mathematics, “sparse” and “dense” often refer to the number of zero vs. non-zero elements in an array (e.g. vector or matrix). A sparse array is one that contains mostly zeros and few non-zero entries. A dense array contains mostly non-zeros.

Why is sparse data a problem?

A common problem in machine learning is sparse data, which alters the performance of machine learning algorithms and their ability to calculate accurate predictions. Data is considered sparse when certain expected values in a dataset are missing, which is a common phenomenon in general large scaled data analysis.

What is a sparse solution?

This is what we mean by a sparse solution – it only uses a few variables in the dataset. Other methods may produce a solution where many variables have small, but non-zero coefficients. These models are not sparse, since you still need all the variables to produce the solution.

How do you determine sparsity?

The number of zero-valued elements divided by the total number of elements (e.g., m × n for an m × n matrix) is called the sparsity of the matrix (which is equal to 1 minus the density of the matrix).