Machine learning predictive modeling performance is only as good as your data, and your data is only as good as the way you prepare it for modeling.
The most common approach to data preparation is to study a dataset and review the expectations of a machine learning algorithms, then carefully choose the most appropriate data preparation techniques to transform the raw data to best meet the expectations of the algorithm. This is slow, expensive, and requires a vast amount of expertise.
An alternative approach to data preparation is to grid search a suite of common and commonly useful data preparation techniques to the raw data. This is an alternative philosophy for data preparation that treats data transforms as another hyperparameter of the modeling pipeline to be searched and tuned.
This approach requires less expertise than the traditional manual approach to data preparation, although it is computationally costly. The benefit is that it can aid in the discovery of non-intuitive data preparation solutions that achieve good or best performance for a given predictive modeling problem.
In this tutorial, you will discover how to use the grid search approach for data preparation with tabular data.
After completing this tutorial, you will know:
- Grid search provides an alternative approach to data preparation for tabular data, where transforms are tried as hyperparameters of the modeling pipeline.
- How to use the grid search method for data preparation to improve model performance over a baseline for a standard classification dataset.
- How to grid search sequences of data preparation methods to further improve model performance.
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Tutorial Overview
This tutorial is divided into three parts; they are:
- Grid Search Technique for Data Preparation
- Dataset and Performance Baseline
- Wine Classification Dataset
- Baseline Model Performance
- Grid Search Approach to Data Preparation
Grid Search Technique for Data Preparation
Data preparation can be challenging.
The approach that is most often prescribed and followed is to analyze the dataset, review the requirements of the algorithms, and transform the raw data to best meet the expectations of the algorithms.
This can be effective but is also slow and can require deep expertise with data analysis and machine learning algorithms.
An alternative approach is to treat the preparation of input variables as a hyperparameter of the modeling pipeline and to tune it along with the choice of algorithm and algorithm configurations.
This might be a data transform that “should not work” or “should not be appropriate for the algorithm” yet results in good or great performance. Alternatively, it may be the absence of a data transform for an input variable that is deemed “absolutely required” yet results in good or great performance.
This can be achieved by designing a grid search of data preparation techniques and/or sequences of data preparation techniques in pipelines. This may involve evaluating each on a single chosen machine learning algorithm, or on a suite of machine learning algorithms.
The benefit of this approach is that it always results in suggestions of modeling pipelines that give good relative results. Most importantly, it can unearth the non-obvious and unintuitive solutions to practitioners without the need for deep expertise.
We can explore this approach to data preparation with a worked example.
Before we dive into a worked example, let’s first select a standard dataset and develop a baseline in performance.
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Dataset and Performance Baseline
In this section, we will first select a standard machine learning dataset and establish a baseline in performance on this dataset. This will provide the context for exploring the grid search method of data preparation in the next section.
Wine Classification Dataset
We will use the wine classification dataset.
This dataset has 13 input variables that describe the chemical composition of samples of wine and requires that the wine be classified as one of three types.
You can learn more about the dataset here:
No need to download the dataset as we will download it automatically as part of our worked examples.
Open the dataset and review the raw data. The first few rows of data are listed below.
We can see that it is a multi-class classification predictive modeling problem with numerical input variables, each of which has different scales.
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