Feature engineering is a part of data science vital for ensuring machine learning models give accurate, reliable, and high -quality outputs. Did you know that data scientists devote as much as 80% of their time on feature engineering? In this blog, organizations will understand the concept of feature engineering, techniques of feature engineering, how to tackle the problem of missing data, benefits of feature engineering and best practices to ensure that feature engineering happens successfully and satisfactorily for your organization.
The Importance of Data in Organizations
With the right data, organizations can leapfrog their competitors. Note that data is the new oil. But the former must be processed such that it is complete, accurate, high-quality as well as relevant. Then data can be supplied to high-quality machine learning models in order to make reliable predictions which are free of bias and empower organizations with key insights for them to make informed decisions.
Understanding Feature Engineering
Feature engineering is the procedure of converting raw data into features that can be utilized in machine learning models. A feature is an input variable utilized by machine learning models to make predictions. As the performance of the model depends on the quality of the data in the training stage, feature engineering plays a valuable role. For your information, feature engineering selects the relevant data out of all the available raw data. Data scientists and machine learning engineers are responsible for feature engineering. Raw data has to be cleaned as well as pre-processed to make it ready for utilization. Here is where feature engineering plays a pivotal role. The goal is to bring some order to existing chaos.
Feature engineering tools fill the gap between raw information and AI-ready data so that entities can maximize the value of their artificial intelligence activities. Each algorithm for ML employs a certain part for input data in order to supply outputs in which the input data comprises of features. Feature engineering is referred to as feature discovery also. For your knowledge, feature extraction involves converting raw data into desired formats. Know that effective feature engineering entails domain expertise, technical knowledge as well as careful planning. The types of features in machine learning include numerical features, categorical features, time-series features and text features. Important features may be spread across multiple data sources. Feature engineering combines the data sources into a sole, usable dataset.
How Feature Engineering Deals with Missing Data
Handling missing data is an important part of feature engineering. If certain columns or rows have excessive missing values then there may be simple deletions. However, that could cause loss of information. The alternative is to place imputing values such as mean, median or mode. A superior technique is leveraging prediction imputation to generate values that could be used in the missing fields.
Feature Engineering Techniques
Feature engineering determines the input quality for machine learning algorithms. Important processes such as feature selection, extraction as well as creation are utilized. Methodologies such as feature scaling as well as one-hot coding transform categorical data into numerical data. Dimensionality reduction methodologies aid in streamlining data, attain superior processing efficiency as well as achieve better model interpretability. Feature engineering involves a lot of trial and error to come up with the perfect results.
Benefits of Feature Engineering
The technique effectively cleans noisy data. This ensures that machine learning models operate at top efficiency. Feature engineering brings down the probability of overfitting. Feature engineering swiftly extracts useful info from text as well as creates potent interaction features. As machine learning models can only process numerical data, feature engineering converts categorical data into numeric data. Feature engineering helps supply high quality data to various machine learning models thus resulting in superior performance. Organizations are able to leverage machine learning models to achieve monetary success as well as ramp up brand image in an intensely competitive market.
Best Practices for Feature Engineering
- Delete Irrelevant Features
In a scenario where the price of a car has to be predicted, the color of the car has very little influence on the prediction. Hence, it can be left out. Deleting irrelevant features simplifies the machine learning model. Also, it enhances the model’s speed as well as interpretability. Moreover, the possibility of overfitting is minimized.
- Create New Features When Necessary
An instance may be predicting home prices. Generating a new feature that multiplies the no of bedrooms by the total floor area could supply useful information to the machine learning model. Fresh features pinpoint data patterns missed by linear models. This measure elevates the performance of the machine learning model.
- Maintain Simplicity
Note that complex features need not be better. First do simple transformations. Only when required introduce complexity.
- Perform Feature Validation
For your information, feature engineering is an iterative process. Validation makes sure that the engineered features result in superior performance of the machine learning model. Pinpoint highly correlated features. Next, get rid of redundancy.
- Analyze Model Interpretation
Employ relevant techniques to comprehend how the machine learning model is utilizing features. This step helps pinpoint areas for improvement in the process of feature engineering.
- Exploratory Data Analysis (EDA)
Conduct rigorous analysis of data distributions, relationships between features as well as potential issues prior to doing feature engineering.
- Utilize One-hot Encoding for Categorical Data
If there is a feature namely color in raw data about automobiles with values ‘black’, ‘orange’ and ‘yellow’, make separate columns for each category. Next, we assign 1 to the column which is correct and 0 to the others.
- Do Scaling and Normalization of Features
In normalization, the data is processed to be between the values zero and one. In standardization, the data is transformed such that the mean is zero and there is unit standard deviation. Techniques such as IQR (Interquartile range) are utilized to scale down the impact of outliers. For your information, IQR is a statistical method utilized in machine learning to measure variability as well as pinpoint outliers in a dataset. Outliers are extreme values that can be too small or too large compared to the rest of the data.
How to Go About Implementing Feature Engineering
If your organization has the in-house expertise to do feature engineering then do so. Otherwise, hire the services of a reputed firm specializing in feature engineering. Remember not to take their word on their quality and performance. Make it a point to ask for references. Speak at length with their past clients to evaluate their quality, performance as well as reliability. Do not make the error of hiring the first competent firm you come across. Have a list of potential outsourcing firms ready. Do due diligence on each of them. Finally, consult the stakeholders of your organization before making the final decision.
With relevant expertise and experience in feature engineering, CoffeeBeans is well positioned to help your organization meet its goals and objectives. We have a pool of professionals proficient in feature engineering. They will ensure that the job is done rapidly, efficiently as well as effectively. Our clients can vouch for our transparency, real-time response as well as adherence to stipulated deadlines. We offer stellar quality at competitive rates. Reach out to us at enquiries@coffeebeans.io to know how we can help your organization obtain its specific and unique requirements as well as preferences.