A data scientist is developing a text classification model using a large corpus of over one million documents. They have generated TF-IDF feature vectors, resulting in a document-term matrix with more than 200,000 unique terms (features). When training a k-Nearest Neighbors (k-NN) classifier on these high-dimensional, sparse vectors, they observe two primary issues: extremely long training times and poor predictive accuracy. Which of the following strategies provides the most effective solution to address both the computational inefficiency and the model performance problem?
Augment the feature set by including bigrams and trigrams from the text corpus.
Convert the TF-IDF matrix into a Compressed Sparse Row (CSR) format.
Standardize the feature vectors using a StandardScaler to have zero mean and unit variance.
Apply Truncated SVD to the feature matrix to reduce its dimensionality.
The correct answer is to apply Truncated SVD (Singular Value Decomposition) to the TF-IDF matrix. The scenario describes a classic problem of high-dimensionality and data sparsity, which leads to two issues. First, the high number of features (200,000+) causes significant computational overhead. Second, distance-based algorithms like k-NN suffer from the 'curse of dimensionality' in high-dimensional spaces, where the distance between points becomes less meaningful, leading to poor model performance. Truncated SVD is a dimensionality reduction technique that is well-suited for sparse matrices like those produced by TF-IDF. It projects the data into a lower-dimensional space, creating dense vectors that capture the most significant latent semantic relationships in the data. This reduction in dimensionality directly addresses both the computational burden and the curse of dimensionality, typically leading to faster training and improved accuracy for the k-NN classifier.
Converting the matrix to a specialized sparse format like CSR only addresses the memory storage and some computational inefficiencies but does not solve the underlying model performance issue caused by the curse of dimensionality. Standardizing the data with StandardScaler does not reduce dimensionality and is generally not applied to sparse matrices as it would destroy sparsity and lead to massive memory consumption. Adding more features like bigrams would further increase the dimensionality and sparsity, worsening both problems.
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