How are sparse datasets used in machine learning?
In this article, we discuss and implement an approach to learning over such sparse, high dimensional datasets. Data dimensionality and data size are two facets of these problems. To reduce data dimensionality, feature hashing offers a scalable and computationally efficient feature representation.
Can you use dimensionality reduction in sparse datasets?
However, these dimensionality reduction techniques, sometimes, cannot be applicable, e.g., in sparse datsets that have independent features and the data lie in multiple lower dimensional manifolds. In this article, we discuss and implement an approach to learning over such sparse, high dimensional datasets.
How to use SciPy for sparse data sets?
Scipy package offers several types of sparse matrices for efficient storage. Sklearn and other machine learning packages such as imblearn accept sparse matrices as input. Therefore, when working with large sparse data sets, it is highly recommended to convert our pandas data frame into a sparse matrix before passing it to sklearn.
How to work with sparse data sets in pandas?
Therefore, when working with large sparse data sets, it is highly recommended to convert our pandas data frame into a sparse matrix before passing it to sklearn. In this example we will use the lil and csr formats. In scipy docs you can see advantages and disadvantages of each format.
How to create an iterator over a dataset?
First, let’s create an iterator object over the dataset The one_shot_iterator method creates an iterator that will be able to iterate once over the dataset. In other words, once we reach the end of the dataset, it will stop yielding elements and raise an Exception.
How does one shot iterator work in graph?
The one_shot_iterator method creates an iterator that will be able to iterate once over the dataset. In other words, once we reach the end of the dataset, it will stop yielding elements and raise an Exception. Now, next_element is a graph’s node that will contain the next element of iterator over the Dataset at each execution.