Parameter Tuning in Weka

Professor Khanh will introduce how to tune hyperparameters in Weka. How to define your model architecture? What the optimal model architecture should be? And how to explore a range of possibilities.

The first session discusses parameter tuning in Weka, focusing on the importance of optimizing parameters in AI, machine learning, and deep learning models. Parameter optimization involves trying various combinations to find the best model. The session explores different questions related to hyperparameter optimization, such as defining model architecture and determining the optimal architecture.

Two common methods for hyperparameter tuning are discussed: trial and error and control experiments. Trial and error involves testing algorithms and parameter sets to select the best performing one, but it can be time-consuming. Control experiments, facilitated by Weka, offer an easier way to perform hyperparameter tuning.

The Weka experimental interface is explained, showcasing functions like opening, saving, and creating new datasets. Users can add algorithms, specify training iterations, and utilize file cross-validation. The session provides examples of algorithms (e.g., zeroR, K-nearest neighbor) and their corresponding parameters that can be adjusted within Weka.

A sample use case using the diabetes dataset is presented, demonstrating how different parameters (e.g., K value, distance function) are evaluated for K-nearest neighbor algorithms. The Weka experimenter is utilized to run and analyze experiments, with accuracy as the evaluation metric. Statistical tests, such as paired t-tests, can be performed to compare the performance of different use cases.

In real applications, conducting multiple experiments and tests is necessary to identify the best-performing configuration.

Practice yourself:

Please follow the teacher’s video or the instructions in the presentation to see what you can do. Exploring the Weka experimental interface and its features for hyperparameter tuning.