Final answer:
To reduce the dimension of a large dataset in a classification problem, one can utilize feature selection techniques and dimensionality reduction algorithms, such as PCA and t-SNE, to retain informative features while reducing computation time.
Step-by-step explanation:
When dealing with a large dataset like the one described, feature selection techniques can be applied to reduce the dimensionality and computation time of the model. One approach is to use statistical algorithms, such as Principal Component Analysis (PCA), to identify and select the most informative features that contribute to the data's variability. By retaining the most significant features and discarding the less informative ones, you can reduce the dimensionality of your dataset. Additionally, you can employ techniques like dimensionality reduction algorithms (e.g., t-SNE, LLE) to find a lower-dimensional representation of the data while preserving important patterns or structures. These methods transform the data to a lower-dimensional space, allowing for faster computations without losing crucial information.