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Which machine learning algorithm is best for sparse data?
Using models that are robust to sparse features Some versions of machine learning models are robust towards sparse data and may be used instead of changing the dimensionality of the data. For example, the entropy-weighted k-means algorithm is better suited to this problem than the regular k-means algorithm.
What are the techniques that can be used to deal with sparse data?
The solution to representing and working with sparse matrices is to use an alternate data structure to represent the sparse data. The zero values can be ignored and only the data or non-zero values in the sparse matrix need to be stored or acted upon.
What is a sparse variable?
A sparse variable is a variable whose values are mostly zero. Because of its sparsity, satisfactory prediction results of a sparse variable usually cannot be obtained by either pure (i.e. single) regression or pure classification machine learning methods.
How is sparse data used in machine learning?
Some versions of machine learning models are robust towards sparse data and may be used instead of changing the dimensionality of the data. For example, the entropy-weighted k-means algorithm is better suited to this problem than the regular k-means algorithm.
Are there any algorithms that accept sparse matrices?
Sklearn has many algorithms that accept sparse matrices. The way to know is by checking the fit attribute in the documentation. Look for this: X: {array-like, sparse matrix}. I will cover several interesting topics in this section.
Can a SciPy sparse array be used for machine learning?
Many linear algebra NumPy and SciPy functions that operate on NumPy arrays can transparently operate on SciPy sparse arrays. Further, machine learning libraries that use NumPy data structures can also operate transparently on SciPy sparse arrays, such as scikit-learn for general machine learning and Keras for deep learning.
What are the two types of sparse features?
On the other hand, if the data is sparse, all the data points are known, but most of them have zero value. To illustrate this point, there are two types of features. The feature with sparse data has known values (= 0), but the feature with missing data has unknown values (= null).