Feature engineering for Machine Learning

Machine Learning is one of the most used modern technologies in our world. Machine Learning helps human a lot to do their jobs at ease. Feature engineering is an important part of Machine Learning. It is a mainly a process in which we find and select features from data set which will be used in building or improving our Machine Learning model which will mainly predict the things. It is one of the primary or basic steps of Machine Learning. So, you can understand how this is important for any ML developer. In this article, we are going to discuss about feature engineering in details.

What is feature?

Before understanding about feature engineering, we have to know what feature is. It is obvious that if any Machine Learning developer wants to m=build one predictive model then he has to use some raw data sets collected from field. By processing this data they build their model. In a data set we can find different rows and columns. Rows represent the instances and columns represent the attributes or variables. Now just think about some data set which consist more than hundred attributes. Now you have to choose the important attributes because only then your model will work smoothly. These attributes are called features. You can think about a data set which consists of the data of different fruits. In this case, the feature may be colour of fruit or the taste of fruit.

What is feature engineering?

Now, we have enough knowledge about feature. So, let’s discuss about feature engineering. It is a technique in which we can select features from our root data set to improve the quality of our machine. We will go through one example to understand this thing. Suppose, one Machine Learning developer wants to work on detection of wine quality. So, he will need one data set to build his model which will predict the quality of wine. In this data set, there may be lots of attributes that varies from wine to wine. So, in this case we need some genuine attributes which controls the wine quality mostly. Here we need feature engineering to do our job. By feature engineering we will detect some genuine attributes or features for our model so, that it works smoothly.

The processes of feature engineering for Machine Learning

There are mainly four processes for feature engineering in Machine Learning. They are as follows:

1. Feature creation
   In this process, we will create features for our predictive Machine Learning model. We will extract the important attributes from data set. It is obvious that this work need human intelligence because we have to decide which attribute will benefit the model most. Innovative ideas will be required to detect the correct features. We can create features from present features by adding, subtracting, dividing or other things. The new feature should be flexible and main attributes of data set. In this way we create new features for our predictive Machine Learning model.
   
2. Transformation
   In this process, we modify the features for improvement of our model. Suppose there is one feature which is not supported by the model then there definitely some error will occur. To avoid this thing, we have to modify the features so that the machine works properly. This process ensures that the Machine Learning model will work with high accuracy and without any error.
   
3. Feature extraction
   This process is automated. In this process of feature engineering, we try to reduce the volume of raw data so that the model of Machine Learning works with more perfection. In this automated method new variables are created from the data set. There are many techniques for this process which are cluster analysis, text analytics, edge detection algorithms, and principal components analysis (PCA).
   
4. Feature selection
   This is an important process of feature engineering. In this process, we select some features from the lots of feature. It is important because if we build a model with all the variables then the performance of the model will reduce. It happens because all the features of the data set are not equivalent for that model. So, we have to find out the necessary features for this model.

Need for feature engineering

We always need a Machine Learning model which will satisfy our desire. But many times it occurs that we find a model which gives us results with low accuracy so, our prediction goes wrong. This thing mainly happens due to the selection of feature. If you are not selecting proper feature then it will happen that you will face wrong predictions. We have discussed more about the need of feature engineering.

1. Better flexibility: If we start to build our Machine Learning model using the data set which is not gone through pre processing means feature selection process then it happens that we have to build our model using complex variables. If we need better model then we have to make the variables flexible. In this thing we can get help from feature engineering process. It increases the flexibility a lot by selecting right features.
2. Making simple model: It is very important for Machine Learning developer to make one model which is very simple because it is easy to make simple model and in that case you can use low cost for maintenance. But just think about a big dataset which has different attributes. If you build a model from this type of attributes then it is obviously going to be a complex model. So, it will be difficult to maintain or improve this type of model. For this reason we need feature engineering.
3. Better result: If you are a machine developer then it is very important for you to make sure that your model gives best prediction. But suppose you are not passing your data set through pre processing then it will give you surely some error in predictions. It happens because some attributes which are not necessary for our model affects the result. So to obtain better result from the model we have to do feature engineering.

Steps in feature engineering

There are different steps in feature engineering. But it is totally vary from person to person. It means what a Machine Learning developer thinks about pre processing data set may not be followed by others. This thing happens often. But obviously there are some general steps to do feature selection. The steps are as follows:

1. Preparation of the data: Firstly we have to prepare our data from raw data set. It actually indicates the process in which we will clean our data. In this process we arrange our data and give it a proper format. This step may include cleaning of data, delivery, data augmentation, fusion, ingestion, or loading.
2. Exploratory analysis: It is an important step in feature engineering. This step is mainly handled by data scientist. In this step we analyse the data and try to find the best possible features so that our Machine Learning model works smoothly. This step may use the data visualisation techniques. It may include the processes like data visualization to do the best for data analysis.
3. Giving benchmark: In this step we set a particular bench mark for different attributes. If the attributes pass the benchmark then only they are selected as features. In this step we try to improve our model’s accuracy for predictions.

Feature engineering techniques

1. Imputation: This technique mainly deals with missing values in the data set. Suppose we are going to build our mode using a data set which has lots of missing value. It will surely damage the accuracy of our model. Imputation helps us here. By this technology we can put missing values by average value or mean or median value of the data. In some cases the highest value is given to missing values.
2. Dealing with outliers: This technique helps us to remove the data points which are deviated from main data set. It means the data points which are far away from the main points of the data set can hamper the model so we will remove it. Feature engineering helps to remove this point.
3. Binning: This technique is used to get rid of big number of parameters. Due to this type of over fitting, our model can get low accuracy. Binning is a good technique in feature engineering to handle this.