“Machine Learning” has become one of the terms we frequently hear lately. However, the concept of “Machine Learning,” which is very important in the machine and robot industry, which develops with the advancement of technology, is one of the most challenging steps.

Machine Learning (ML) as a scientific endeavor has historically emerged from the search for Artificial Intelligence (AI). Some academic studies showed that machines have to learn the data after a particular stage, so researchers have conducted investigations to approach the problems that arise on this subject with various symbolic methods. The probabilistic logic technique was also used, especially in automatic medical diagnosis systems.

As a separate field, Machine Learning (ML) started to develop again in the 1990s. The goal of field change is to catch Artificial Intelligence (AI) in handling solvable problems in practical life.

These methods can be distinguished in general terms as follows:

► Machine Learning (ML) focuses on predictions made from learned data based on available features.

► On the other hand, data mining focuses on discovering unknown features in (past) data. This is a step of knowledge discovery analysis in databases.

These two areas overlap in many ways. For example, data mining uses many Machine Learning (ML) methods but often has different goals. On the other hand, Machine Learning (ML) also employs data mining methods such as unsupervised learning or the preprocessing step to improve learner accuracy.

In a Machine Learning (ML) process, the feature selection step of input variables becomes essential depending on our target variable.

The variable selection primarily focuses on removing non–explanatory or unnecessary predictive elements from the model.

What is the Difference Between Variable Selection and Feature Engineering?

Feature Engineering, for example, in a Machine Learning (ML) process, either changes the form of the variables in the data set or generates new and different variables. For instance, it can derive a new variable by interpreting the values ​​in the text field within a variable.

If we give a different example; if we compare the variables to the clothes in a wardrobe; In the feature selection method, you can take one outfit and leave the other, while in Feature Engineering, for example, you can take a suit and put it in the closet as different variables, such as shirts, trousers, and jackets.

“Provides the most important step in your data science workflow”

The most critical step in the data science workflow is one of the steps that bring the most value.

Mastering this takes a lot of work and practice: you need to understand the mathematical principles underlying property transformations to manipulate them and know what types of changes apply in each case.

Feature Engineering is the process of selecting, modifying, and transforming raw data into features that can be used in supervised learning. To make Machine Learning (ML) work well in new tasks, it may be necessary to design and train better features. As you know, a “feature” is any measurable input that can be used in a predictive model – be it the color of an object or the sound of someone—transforming.

Feature Engineering consists of several processes:

Benchmark: A Benchmark model is a reliable, transparent, and interpretable model that you can measure yourself. It’s a good idea to test datasets to see if your new Machine Learning (ML) model beats a recognized Benchmark. These benchmarks are often used to compare performance between Machine Learning (ML) models such as neural networks and support vector machines, linear and nonlinear classifiers, or other approaches such as bagging and augmentation.

Feature Creation: Feature creation involves creating new variables that will be most useful to our model. This could be adding or removing some features. As we saw above, the cost per square foot column was a feature creation.

Transformations: A property transformation is a function that converts properties from one representation to another. The point here is to plot and visualize data; if nothing is added with new features, we can reduce the number of components used, speed up training, or increase the accuracy of a particular model.

Feature Extraction: Feature extraction extracts features from a dataset to identify helpful information. Without breaking original relationships or essential news, this compresses the amount of data into manageable amounts for algorithms to process.

Exploratory Data Analysis: In addition, it is often used on large amounts of qualitative or quantitative data that have not been analyzed before.

Feature Engineering is a crucial step in Machine Learning. This algorithm then uses these artificial features to increase its performance, or in other words, to achieve better results.

“Getting models right becomes essential.”

Includes all the essential factors affecting the business problem. As a result of these data sets, the most accurate prediction models and valuable insights are produced.

Feature Engineering Techniques for Machine Learning

Let’s see a few Feature Engineering best techniques you can use. Some of the methods listed may work better with specific algorithms or datasets, while others may be useful in any situation.

1. Imputation

In addition, missing values ​​impact the performance of Machine Learning (ML) models for some reason. The primary purpose of the imputation is to address these missing values.

There are two types of assignments:

1. Numerical Estimation:  These datasets may contain information about how many people living in a city or country with a cold climate eat different types of food and how much they earn each year. Therefore, numerical evaluation is used to fill in the gaps in surveys or censuses when certain pieces of information are missing.
2. Categorical Prediction: When dealing with flat columns, it’s an intelligent solution to replace missing values ​​with the highest value in the queue. However, if you think that the values ​​in the column are evenly distributed and there is no dominant value, it would be better to assign a category such as “Other,” as your assignment is more likely to converge to a random selection in this scenario.
3. Handling Outliers

Outlier processing is a technique of removing outliers from a dataset. This method can be used at various scales to produce a more accurate data representation. This affects the performance of the model. Depending on the model, the effect can be significant or minimal, for example, linear regression is sensitive to outliers. This procedure must be completed before model training.

Various methods of handling outliers include:

1. Removal: Entries with outliers are deleted from the distribution. However, if there are outliers among many variables, this strategy can result in a large portion of the datasheet being missed.
2. Limitation: Using a random value or a value from a distribution of variables to swap the maximum and minimum values.
3. Discretization: Discretization is converting continuous variables, models, and functions into discrete ones. This is accomplished by creating a series of constant ranges (or boxes) covering the desired degree of variables/patterns/functions.
4. Changing Values: Alternatively, outliers can be treated as missing values ​​and replaced with an appropriate assignment.

It is mainly used to transform a skewed distribution into a normal or less uneven distribution. We log the values ​​in a column and use these values ​​as columns in this conversion. It manipulates confusing data, and the data becomes closer to standard applications.

3. Scaling

Feature scaling is one of the most common and complex problems in Machine Learning (ML). While this step isn’t necessary for many algorithms, it’s still a good idea to do it.

1. Normalization: All values ​​are scaled within a specified range from 0 to 1 through normalization (or min–max normalization). This change does not affect the distribution of the feature but exacerbates the effects of outliers due to lower standard deviations. Consequently, it is recommended to handle outliers before normalization.
2. Standardization: Standardization (also known as z–score normalization) is the process of scaling values ​​while taking standard deviation into account. If the standard deviation of the features is different, the range of these features will likewise be diverse. As a result, the effect of outliers on the parts is reduced. To arrive at a distribution with a mean of 0 and a variance of 1, all data points are subtracted from their standard, and the result is divided by the variance of the distribution.