Mastering and Tuning Decision Trees Assignment Help

IBM SPSS Modeler is a data mining workbench that allows you to quickly and intuitively build predictive models without programming. Students will learn about the situations in which this technique would be useful, its assumptions, how to perform the analysis automatically as well as interactively, and how to interpret the results.

KEY FEATURE

Mastering and Tuning Decision Trees is a self-paced video series that discusses the IBM SPSS Modeler decision tree methods (CHAID, C5.0, CRT, and QUEST).

Based on the values of the predictor variables, these techniques generate a rule-based predictive model for an outcome variable.

Students will learn about the situations in which this technique would be useful, its assumptions, how to perform the analysis automatically as well as interactively, and how to interpret the results.

The contrast between CHAID and C&RT is highlighted in detail. Both CHAID and C&RT are used to demonstrate tuning, which is the process of adjusting parameters to optimise performance.

Mastering and Tuning Decision Trees Assignment Help

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Key Topics

    Hyperparameter Tuning

    The process of searching the hyperparameter space for a set of values that will optimise your model architecture is known as parameter tuning.
    This is distinct from tuning your model parameters, in which you search your feature space for the feature with the lowest cost function.
    Hyperparameter tuning is also difficult because there is no direct way to calculate how a change in hyperparameter value will reduce your model’s loss, so we usually rely on experimentation. We begin by specifying a range of possible values for each hyperparameter. This is where most people get stuck: what values should I try? To answer that question, you must first understand what these hyperparameters mean and how changing one will affect your model architecture, and thus try to understand how your model performance may change.
    Following the definition of the range of values, the next step is to use a hyperparameter tuning method. There are several, the most common and expensive being Grid Search, while others such as Random Search and Bayesian Optimization will provide a “smarter,” less expensive tuning.

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