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The data has been cleaned by the encoding of categorical variables, transforming variables and the detection of missing values. The output of the descriptive statistics is provided below, a summary of all of the numeric variables and missing values. From the glimpse of the structure above, some of the data attributes were not cast in their correct data type. This is my favourite part the chance to transform the variables!

Here we go…. We will try a few methods to detect the missing values such as counting the number of missing values per column, sum and taking the mean.

We installed Amelia package via install. From the map we observe the missing values are detected from the following columns:. View a view the missing values complete. The imputed data was plotted to understand the distribution of the original data. The techniques for imputation that I followed were from the author Michy Alice from his blog. These are the steps:.

Multiple Imputation in SPSS via OMS procedure: get a final single dataset!

One advantage that multiple imputation has over the single imputation and complete case methods is that multiple imputation is flexible and can be used in a wide variety of scenarios. Multiple imputation can be used in cases where the data is missing completely at random , missing at random , and even when the data is missing not at random. However, the primary method of multiple imputation is multiple imputation by chained equations MICE. It is also known as "fully conditional specification" and, "sequential regression multiple imputation.

As alluded in the previous section, single imputation does not take into account the uncertainty in the imputations. After imputation, the data is treated as if they were the actual real values in single imputation. The negligence of uncertainty in the imputation can and will lead to overly precise results and errors in any conclusions drawn. Additionally, while it is the case that single imputation and complete case are easier to implement, multiple imputation is not very difficult to implement.

There are a wide range of different statistical packages in different statistical software that readily allow someone to perform multiple imputation. From Wikipedia, the free encyclopedia. Process of replacing missing data with substituted values.

Towards Data Science

For other uses of "imputation", see Imputation disambiguation. Main article: Listwise deletion. Statistical Methods in Medical Research. Cambridge University Press, Clinical Investigation. Applied Missing Data Analysis. New York: Guilford Press. Canadian Medical Association Journal. SAS Institute Inc. Multiple Imputation". Flexible Imputation of Missing Data.

MaCH: machX

International Journal of Methods in Psychiatric Research. Journal of Classification. Annual Review of Psychology. Even numeric features can have similar distributions. However, lumping should be used sparingly as there is often a loss in model performance Kuhn and Johnson Tree-based models often perform exceptionally well with high cardinality features and are not as impacted by levels with small representation. Many models require that all predictor variables be numeric. Consequently, we need to intelligently transform any categorical variables into numeric representations so that these algorithms can compute.

Some packages automate this process e. There are many ways to recode categorical variables as numeric e. The most common is referred to as one-hot encoding, where we transpose our categorical variables so that each level of the feature is represented as a boolean value.

For example, one-hot encoding the left data frame in Figure 3. This is called less than full rank encoding.

Missing Value Imputation Transformer

However, this creates perfect collinearity which causes problems with some predictive modeling algorithms e. Alternatively, we can create a full-rank encoding by dropping one of the levels level c has been dropped. This is referred to as dummy encoding. Since one-hot encoding adds new features it can significantly increase the dimensionality of our data. If you have a data set with many categorical variables and those categorical variables in turn have many unique levels, the number of features can explode.


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Label encoding is a pure numeric conversion of the levels of a categorical variable. If a categorical variable is a factor and it has pre-specified levels then the numeric conversion will be in level order. If no levels are specified, the encoding will be based on alphabetical order. We should be careful with label encoding unordered categorical features because most models will treat them as ordered numeric features. If a categorical feature is naturally ordered then label encoding is a natural choice most commonly referred to as ordinal encoding.

Ordinal encoding these features provides a natural and intuitive interpretation and can logically be applied to all models. There are several alternative categorical encodings that are implemented in various R machine learning engines and are worth exploring. For example, target encoding is the process of replacing a categorical value with the mean regression or proportion classification of the target variable. Target encoding runs the risk of data leakage since you are using the response variable to encode a feature. An alternative to this is to change the feature value to represent the proportion a particular level represents for a given feature.

In Chapter 9, we discuss how tree-based models use this approach to order categorical features when choosing a split point. Several alternative approaches include effect or likelihood encoding Micci-Barreca ; Zumel and Mount , empirical Bayes methods West, Welch, and Galecki , word and entity embeddings Guo and Berkhahn ; Chollet and Allaire , and more. For more in depth coverage of categorical encodings we highly recommend Kuhn and Johnson Dimension reduction is an alternative approach to filter out non-informative features without manually removing them.

We discuss dimension reduction topics in depth later in the book Chapters 17 - 19 so please refer to those chapters for details. However, we wanted to highlight that it is very common to include these types of dimension reduction approaches during the feature engineering process.

We stated at the beginning of this chapter that we should think of feature engineering as creating a blueprint rather than manually performing each task individually. This helps us in two ways: 1 thinking sequentially and 2 to apply appropriately within the resampling process. Thinking of feature engineering as a blueprint forces us to think of the ordering of our preprocessing steps.

Although each particular problem requires you to think of the effects of sequential preprocessing, there are some general suggestions that you should consider:. Data leakage is when information from outside the training data set is used to create the model. Data leakage often occurs during the data preprocessing period. To minimize this, feature engineering should be done in isolation of each resampling iteration.

Recall that resampling allows us to estimate the generalizable prediction error. Therefore, we should apply our feature engineering blueprint to each resample independently as illustrated in Figure 3. That way we are not leaking information from one data set to another each resample is designed to act as isolated training and test data. For example, when standardizing numeric features, each resampled training data should use its own mean and variance estimates and these specific values should be applied to the same resampled test set. The recipes package allows us to develop our feature engineering blueprint in a sequential nature.

The idea behind recipes is similar to caret::preProcess where we want to create the preprocessing blueprint but apply it later and within each resample. There are three main steps in creating and applying feature engineering with recipes :.

The first step is where you define your blueprint aka recipe. We then:.

Add the tool

Next, we need to train this blueprint on some training data. Remember, there are many feature engineering steps that we do not want to train on the test data e. So in this step we estimate these parameters based on the training data of interest. Lastly, we can apply our blueprint to new data e. Consequently, the goal is to develop our blueprint, then within each resample iteration we want to apply prep and bake to our resample training and validation data.

Luckily, the caret package simplifies this process. We only need to specify the blueprint and caret will automatically prepare and bake within each resample. We illustrate with the ames housing example.

Multiple imputation in the presence of non-normal data.

Next, we apply the same resampling method and hyperparameter search grid as we did in Section 2. The only difference is when we train our resample models with train , we supply our blueprint as the first argument and then caret takes care of the rest. The chapters that follow will look to see if we can continue reducing our error by applying different algorithms and feature engineering blueprints.

Series B Methodological. JSTOR, — Breiman, Leo, and others. Institute of Mathematical Statistics: — Carroll, Raymond J, and David Ruppert. Oxford University Press: — Deep Learning with R. Manning Publications Company. Gower, John C. Elsevier: 83— Guo, Cheng, and Felix Berkhahn. Kuhn, Max, and Kjell Johnson.

Statistical Analysis with Missing Data. Elsevier: — Micci-Barreca, Daniele. ACM: 27—