There are a vast number of different types of data preparation techniques that could be used on a predictive modeling project.
In some cases, the distribution of the data or the requirements of a machine learning model may suggest the data preparation needed, although this is rarely the case given the complexity and high-dimensionality of the data, the ever-increasing parade of new machine learning algorithms and limited, although human, limitations of the practitioner.
Instead, data preparation can be treated as another hyperparameter to tune as part of the modeling pipeline.
This raises the question of how to know what data preparation methods to consider in the search, which can feel overwhelming to experts and beginners alike.
The solution is to think about the vast field of data preparation in a structured way and systematically evaluate data preparation techniques based on their effect on the raw data.
In this tutorial, you will discover a framework that provides a structured approach to both thinking about and grouping data preparation techniques for predictive modeling with structured data.
After completing this tutorial, you will know:Discover data cleaning, feature selection, data transforms, dimensionality reduction and much more in my new book, with 30 step-by-step tutorials and full Python source code.
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Framework for Data Preparation Techniques in Machine LearningPhoto by Phil Dolby, some rights reserved.
This tutorial is divided into three parts; they are:Data preparation refers to transforming raw data into a form that is better suited to predictive modeling.
This may be required because the data itself contains mistakes or errors.
It may also be because the chosen algorithms have expectations regarding the type and distribution of the data.
To make the task of data preparation even more challenging, it is also common that the data preparation required to get the best performance from a predictive model may not be obvious and may bend or violate the expectations of the model that is being used.
As such, it is common to treat the choice and configuration of data preparation applied to the raw data as yet another hyperparameter of the modeling pipeline to be tuned.
This framing of data preparation is very effective in practice, as it allows you to use automatic search techniques like grid search and random search to discover unintuitive data preparation steps that result in skillful predictive models.
This framing of data preparation can also feel overwhelming to beginners given the large number and variety of data preparation techniques.
The solution to this overwhelm is to think about data preparation techniques in a systematic way.
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Download Your FREE Mini-CourseEffective data preparation requires that the data preparation techniques available are organized and considered in a structured and systematic way.
This allows you to ensure that approach techniques are explored for your dataset and that potentially effective techniques are not skipped or ignored.
This can be achieved using a framework to organize data preparation techniques that consider their effect on the raw dataset.
For example, structured machine learning data, such as data we might store in a CSV file for classification and regression, consists of rows, columns, and values.
We might consider data preparation techniques that operate at each of these levels.
Data preparation for rows may be techniques that add or remove rows of data from the dataset.
Similarly, data preparation for columns may be techniques that add or remove rows (features or variables) from the dataset.
Whereas data preparation for values may be techniques that change the values in the dataset, often for a given column.
There is one more type of data preparation that does not neatly fit into this structure, and that is dimensionality reduction techniques.
These techniques change the columns and the values at the same time, e.
g.
projecting the data into a lower-dimensional space.
This raises the question of techniques that might apply to rows and values at the same time.
This might include data preparation that consolidates rows of data in some way.
We can summarize this framework and some high-level groups of data preparation methods in the following image.
Machine Learning Data Preparation FrameworkNow that we have a framework for thinking about data preparation based on their effect on the data, let’s look at examples of techniques that fit into each group.
This section explores the five high-level groups of data preparation techniques defined in the previous section and suggests specific techniques that may fall within each group.
Did I miss one of your preferred or favorite data preparation techniques? Let me know in the comments below.
This group is for data preparation techniques that add or remove rows of data.
In machine learning, rows are often referred to as samples, examples, or instances.
These techniques are often used to augment a limited training dataset or to remove errors or ambiguity from the dataset.
The main class of techniques that come to mind are data preparation techniques that are often used for imbalanced classification.
This includes techniques such as SMOTE that create synthetic rows of training data for under-represented classes and random undersampling that remove examples for over-represented classes.
For more on SMOTE data sampling, see the tutorial:It also includes more advanced combined over- and undersampling techniques that attempt to identify and remove ambiguous examples along the decision boundary of a classification problem and remove them or change their class label.
For more on these types of data preparation, see the tutorial:This class of data preparation techniques also includes algorithms for identifying and removing outliers from the data.
These are rows of data that may be far from the center of probability mass in the dataset and, in turn, may be unrepresentative of the data from the domain.
For more on outlier detection and removal methods, see the tutorial:This group is for data preparation techniques that add or remove columns of data.
In machine learning, columns are often referred to as variables or features.
These techniques are often required to either reduce the complexity (dimensionality) of a prediction problem or to unpack compound input variables or complex interactions between features.
The main class of techniques that come to mind are feature selection techniques.
This includes techniques that use statistics to score the relevance of input variables to the target variable based on the data type of each.
For more on these types of data preparation techniques, see the tutorial:This also includes feature selection techniques that systematically test the impact of different combinations of input variables on the predictive skill of a machine learning model.
For more on these types of methods, see the tutorial:Related are techniques that use a model to score the importance of input features based on their use by a predictive model, referred to as feature importance methods.
These methods are often used for data interpretation, although they can also be used for feature selection.
For more on these types of methods, see the tutorial:This group of methods also brings to mind techniques for creating or deriving new columns of data, new features.
These are often referred to as feature engineering, although sometimes the whole field of data preparation is referred to as feature engineering.
For example, new features that represent values raised to exponents or multiplicative combinations of features can be created and added to the dataset as new columns.
For more on these types of data preparation techniques, see the tutorial:This might also include data transforms that change a variable type, such as creating dummy variables for a categorical variable, often referred to as a one-hot encoding.
For more on these types of data preparation techniques, see the tutorial:This group is for data preparation techniques that change the raw values in the data.
These techniques are often required to meet the expectations or requirements of specific machine learning algorithms.
The main class of techniques that come to mind is data transforms that change the scale or distribution of input variables.
For example, data transforms such as standardization and normalization change the scale of numeric input variables.
Data transforms like ordinal encoding change the type of categorical input variables.
There are also many data transforms for changing the distribution of input variables.
For example, discretization or binning change the distribution of numerical input variables into categorical variables with an ordinal ranking.
For more on this type of data transform, see the tutorial:The power transform can be used to change the distribution of data to remove a skew and make the distribution more normal (Gaussian).
For more on this method, see the tutorial:The quantile transform is a flexible type of data preparation technique that can map a numerical input variable or to different types of distributions such as normal or Gaussian.
You can learn more about this data preparation technique here:Another type of data preparation technique that belongs to this group are methods that systematically change values in the dataset.
This includes techniques that identify and replace missing values, often referred to as missing value imputation.
This can be achieved using statistical methods or more advanced model-based methods.
For more on these methods, see the tutorial:All of the methods discussed could also be considered feature engineering methods (e.
g.
fitting into the previously discussed group of data preparation methods) if the results of the transforms are appended to the raw data as new columns.
This group is for data preparation techniques that change both the number of columns and the values in the data.
The main class of techniques that this brings to mind are dimensionality reduction techniques that specifically reduce the number of columns and the scale and distribution of numerical input variables.
This includes matrix factorization methods used in linear algebra as well as manifold learning algorithms used in high-dimensional statistics.
For more information on these techniques, see the tutorial:Although these techniques are designed to create projections of rows in a lower-dimensional space, perhaps this also leaves the door open to techniques that do the inverse.
That is, use all or a subset of the input variables to create a projection into a higher-dimensional space, perhaps decompiling complex non-linear relationships.
Perhaps polynomial transforms where the results replace the raw dataset would fit into this class of data preparation methods.
Do you know of other methods that fit into this group? Let me know in the comments below.
This group is for data preparation techniques that change both the number of rows and the values in the data.
I have not explicitly considered data transforms of this type before, but it falls out of the framework as defined.
A group of methods that come to mind are clustering algorithms where all or subsets of rows of data in the dataset are replaced with data samples at the cluster centers, referred to as cluster centroids.
Related might be replacing rows with exemplars (aggregates of rows) taken from specific machine learning algorithms, such as support vectors from a support vector machine, or the codebook vectors taken from a learning vector quantization.
Naturally, these aggregate rows are simply added to the dataset rather than replacing rows, then they would naturally fit into the “Data Preparation for Rows” group described above.
Do you know of other methods that fit into this group? Let me know in the comments below.
This section provides more resources on the topic if you are looking to go deeper.
In this tutorial, you discovered a framework for systematically grouping data preparation techniques based on their effect on raw data.
Specifically, you learned:Do you have any questions? Ask your questions in the comments below and I will do my best to answer.
with just a few lines of python codeDiscover how in my new Ebook: Data Preparation for Machine LearningIt provides self-study tutorials with full working code on: Feature Selection, RFE, Data Cleaning, Data Transforms, Scaling, Dimensionality Reduction, and much more.
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