Final answer:
It is false that using a large number of smaller bins is always better for discretizing age. The appropriate number of bins depends on various factors such as data distribution, range, and the goal of the analysis, which can necessitate a larger or smaller number of bins for an accurate representation.
Step-by-step explanation:
It is false that for discretizing the age attribute, it is always better to use a large number of smaller bins as opposed to a small number of larger bins. The choice between a large number of smaller bins and a smaller number of larger bins depends on the specific characteristics and the range of the data set being analyzed. Discretizing into too many bins might result in very sparse data within each bin, which can lead to overfitting and poor generalization to new data points. Conversely, using too few bins might oversimplify the data and lose important nuances and trends.
The goal is to balance detail with generalization, which can vary depending on the distribution of the data, the range of values, and the objective of the analysis. Factors such as the overall range of data, potential outliers, and the shape of the data distribution should be considered when determining the appropriate number of bins.
For example, if we have a small range of data, we might prefer a smaller number of bins to avoid having bins with very few data points. Alternatively, if the data is skewed or has outliers, more bins may be helpful to depict the variability more accurately. Thus, the discretization process should be tailored to fit the specific context and requirements of the data analysis task at hand.