Data Research and Feature Engineering

The costliest aspect of most AI and ML efforts is sourcing, cleansing, and organizing the data. We have seasoned data experts across most asset classes and expertise with alternative data sources. We help evaluate financial data sets and apply a wide range of alternative data, structured and unstructured, to predict and quantify financial outcomes.

Feature engineering for machine learning is essential for utilizing raw data inputs to create machine learning models. This is when feature engineering truly shines.

Feature engineering for machine learning has two primary goals – the preparation of a proper input dataset that is compatible with the requirements of the machine learning algorithm and improving the overall performance of the machine learning models.

How It Works

Feature engineering for machine learning makes the input data fitter for the current machine learning algorithm, making operations more efficient and error-free. Feature engineers can be of great help to data scientists. Feature engineering methods can significantly accelerate the extraction of valuable variables from the data.

Data scientists will be able to extract and utilize more variables in the process.

Feature engineers can examine large tracts of data and provide analysis on problems and figure out how the data can solve these problems.

Feature engineers can also define features through feature extraction (defining different sets of elements representing the data in relation to the analysis) and feature construction, which involves transforming the set of input features to create another, newer set of features that can then be used for prediction.  

There are seven main areas of feature engineering for machine learning that are most vital.

Imputation

One of the most common issues in data preparation is missing values. Missing values arise from the interruption of data flow, human error, and sometimes even privacy concerns. On the whole, missing values will affect the overall performance of machine learning models. Several kinds of imputations can be performed:

1.      Numerical imputation is preferred over dropping because this operation helps preserve the current data size. However, care must be given in selecting what to impute.

2.      Categorical imputation refers to replacing the missing values with the maximum occurred value within a given column. This is an ideal option for categorical columns.

3.      Random sample imputation focuses on random observation from the present dataset. We then use these random observations to impute NaN values.

Handling Outliers

Outliers are data points that are usually three or more standard deviations away from the mean. This is a basic definition for an outlier, but there are many more possible definitions depending on the situation.

Outliers can either be true outliers or an error in the data. Outliers can be as simple as having typed 9000 rather than 90. A true outlier can be represented by something like finding Elon Musk in your dataset. However, due to the big difference, the mere presence of the outlier can skew the data. One of the best ways to detect outliers is by visually representing the data. Visualizing data allows you to identify the outliers quickly and with precision.

Binning

This operation can be done on both numerical and categorical data. The goal is to make the model robust and to avoid instances of overfitting. However, binning can have a negative impact – it can reduce performance. Whenever binning is performed, information is sacrificed in the process, but the data is regularized in return.

Log Transform

Logarithmic transformation is a common transformation used in feature engineering for machine learning. Log transform helps process skewed data. After the logarithmic transformation, the present distribution becomes more proximate to normal. Log transform also reduces the overall impact of outliers through the normalization of the differences in magnitude. The model becomes robust. Log transform must only be applied to data with positive values to prevent errors from surfacing.

One-Hot Encoding

One-hot encoding allows for the distribution of values in a given column to numerous flag columns before assigning 0 or 1 values. The binary assignations provide the associations or relationships between the newly encoded column and the grouped column. One-hot encoding changes the nature of your categorical data. It transforms it to a numerical format so you can group your categorical data without running the risk of losing any of the data.

Grouping Operations

A row often represents instances in the current training dataset. The different columns provide different features of each instance. This type of categorization is called Tidy. Tidy datasets are easier to visualize, model, and manipulate, and they also have specific structures.

Each column represents a variable, observations are assigned rows, and observational units are assigned tables. Group operations decide the various aggregation of functions of the said features. As for numerical features, the sum functions and average are ideal choices. Categorical features are decidedly more complicated.

There are three ways of aggregating categorical columns. The first option is labeling with the highest frequency. This would be the max operation for the current categorical columns. The max functions in general will not return such values. You would have to use the lambda function. The second option is to create a pivot table. This operation would resemble the encoding method. The definition would be the aggregated functions between the encoded and grouped columns. The second option is ideal if you wish to move beyond binary flag columns or if you wish to merge multiple features into aggregated features. Aggregated features are more informative.

Feature Split

The feature split is helpful for machine learning. The majority of the time, the input data for machine learning violates the basic principles of the Tidy method. Splitting the features into usable parts allows you to:

-          Make binning possible so you can group the data

-          Enable ML algorithms to comprehend the split features

-          Improve the performance of the model through the uncovering of potential information

There isn’t a golden rule for splitting features. The method used would be aligned with the column’s characteristics.