Need for Feature Engineering in Machine Learning

In machine learning, the performance of the model depends on data pre-processing and data handling. But if we create a model without pre-processing or data handling, then it may not give good accuracy. Whereas, if we apply feature engineering on the same model, then the accuracy of the model is enhanced. Hence, feature engineering in machine learning improves the model's performance. Below are some points that explain the need for feature engineering:

Better features mean flexibility.
In machine learning, we always try to choose the optimal model to get good results. However, sometimes after choosing the wrong model, still, we can get better predictions, and this is because of better features. The flexibility in features will enable you to select the less complex models. Because less complex models are faster to run, easier to understand and maintain, which is always desirable.

Better features mean simpler models.
If we input the well-engineered features to our model, then even after selecting the wrong parameters (Not much optimal), we can have good outcomes. After feature engineering, it is not necessary to do hard for picking the right model with the most optimized parameters. If we have good features, we can better represent the complete data and use it to best characterize the given problem.

Better features mean better results.
As already discussed, in machine learning, as data we will provide will get the same output. So, to obtain better results, we must need to use better features.

Steps in Feature Engineering

The steps of feature engineering may vary as per different data scientists and ML engineers. However, there are some common steps that are involved in most machine learning algorithms, and these steps are as follows:

Data Preparation: The first step is data preparation. In this step, raw data acquired from different resources are prepared to make it in a suitable format so that it can be used in the ML model. The data preparation may contain cleaning of data, delivery, data augmentation, fusion, ingestion, or loading.

Exploratory Analysis: Exploratory analysis or Exploratory data analysis (EDA) is an important step of features engineering, which is mainly used by data scientists. This step involves analysis, investing data set, and summarization of the main characteristics of data. Different data visualization techniques are used to better understand the manipulation of data sources, to find the most appropriate statistical technique for data analysis, and to select the best features for the data.

Benchmark: Benchmarking is a process of setting a standard baseline for accuracy to compare all the variables from this baseline. The benchmarking process is used to improve the predictability of the model and reduce the error rate.

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