Impact of sample size on optimisation algorithms for the MLP used in the prediction of client subscription to a term deposit.

Key Points

• This research aims to understand how varying sample sizes affect the performance of optimisation algorithms used for predicting client subscriptions to term deposits.
• Analyzed classification performance of various prediction algorithms.
• Examined the relationship between sample size and algorithm performance metrics.
• Proposed future comparisons with previously identified classifiers.
• No consistent trend in classification performance with increasing sample size was observed.
• Performance metrics showed significant fluctuations across different sample sizes.