Every machine learning algorithm analyzes and processes input data and generates the outputs. The input data includes features in columns. These columns are structured for categorization. Algorithms will require some features and characteristics to function properly. Here are the two main goals of feature engineering:
- The feature engineering will improve the performance of the model
- Prepare relevant input data that is compatible with the requirements of the algorithm
The feature engineering enables you to transform raw data into features. These features highlight predictive models’ issues. Therefore, you can solve these problems and improve the accuracy of the model for new data. Feature engineering helps with the
- The performance measure of the model
- Framing the problem
- Predicting the output of the models
- Sampling, formatting, and cleaning the raw data
Importance of Feature Engineering
The features in the model will influence predictive models, resulting in accurate usage and outcome. You should prepare and choose better features for better results. The output you choose, the features you provide, and the data are all the factors of your model. The objective of the model and framing of the problem will also estimate the accuracy of the project. There are numerous inter-dependent properties, and your result will depend on them. You should have relevant features and define the structure of your data.
Flexibility with Better Features
You can achieve good results with the wrong models. Most models will create an optimal data structure. The flexibility of the features enables you with less complicated models. These models are easy to understand, effortless to maintain, and fast when performing activities.
Simpler Models with Better Features
When your model contains well-engineered features, it provides an effective outcome, even if the purpose of the model is the same. You will not require much time and effort to choose the right models and optimize the parameters. Good features will offer you a close analysis of underlying problems. Also, it helps with the classification of the data and underlying problems.
List of Feature Engineering Techniques
When gathering the data for your machine learning project, you will encounter common missing data problems. Missing data issues arise because of human error, privacy concerns, and interruption of the data flow. No matter what the reason, missing values will affect the performance of machine learning models. You can solve this problem by dropping columns and rows, increasing the threshold.
You can detect and handle outliers by visualizing the data. With this technique, you can make high-precision decisions and reduce mistakes. Statistical methodologies are fast and superior but offer less precision. You can handle the outlier using percentile and standard deviation methods.
Binning factors can help with numerical and categorical data. You can develop a robust model by utilizing the motivation of binning and prevent overfitting. Whenever you bin the information, you regularize the data. A key point of the binning process is the trade-off between overfitting and performance.
Log transformation is common in feature engineering. After the transformation, you can handle the skewed data, and the data distribution will be normal. Also, the log transformation will reduce the effect of outliers. This will make the model more robust because of the normalization of magnitude differences.
This encoding method is one of the most common techniques in machine learning. One-Hot encoding will spread the values into multiple flag columns. Furthermore, it assigns 0 or 1 to each value. With the help of these binary values, the model expresses a relationship between encoded and grouped columns.
The main purpose of the grouping operation is to choose the aggression functions. Convenient options for aggregation functions of the features include average and sum.
You can use splitting features to utilize the dataset in the machine learning process. Datasets usually include string columns violating the tidy data principles. When you extract sections of the columns into different and new features, you can:
- Utilize the machine learning algorithm and comprehend the data
- Bin and group the data
- Improve the performance of the model by revealing potential information
The numerical features of the data are usually different from each other and do not include a certain range. If you consider this in a real example, the income and age columns cannot have the same range. However, when we consider this problem from the machine learning model, the comparison is possible. You can solve the problem with the help of scale. After the scaling process, continuous features will have a similar range. Algorithms for calculating the distance, such as k-Means or k-NN, have scaled continuous features as the input of the model.
The date column provides essential information about the model. Many professionals neglect the data as input and do not utilize it in the machine learning algorithms. If you leave the dates without manipulation, you will find it challenging to develop a relationship between models. Therefore, you can utilize feature engineering to extract dates and specify them as a feature.
Feature engineering enables modern deep learning methods, such as restricted Boltzmann machines and autoencoders, to achieve success. These models are automatic but perform the function as semi-supervised or unsupervised methods. Furthermore, it helps to learn abstract visualization of the features, generate high-quality outputs for image classification, speech recognition, object recognition, and other areas.