20 Feature Engineering Interview Questions and Answers

Feature engineering is a process of creating new features from existing data. This process is often used in machine learning and data mining applications to improve the predictive power of models. In a job interview, you may be asked questions about your experience with feature engineering. Answering these questions confidently can help you demonstrate your skills and knowledge to the hiring manager. In this article, we review some common feature engineering interview questions and provide tips on how to answer them.

Feature Engineering Interview Questions and Answers

Here are 20 commonly asked Feature Engineering interview questions and answers to prepare you for your interview:

1. What is feature engineering?

Feature engineering is the process of taking raw data and transforming it into features that can be used in machine learning models. This can involve a variety of different techniques, but the goal is always to create features that are useful for predictive modeling. This can be a difficult process, as it requires a good understanding of both the data and the machine learning algorithms that will be used.

2. Can you explain what the term “feature” means in machine learning?

In machine learning, a feature is an individual measurable property or characteristic of a phenomenon being observed. In other words, features are the variables that will be used in training a machine learning model. When choosing features, it is important to select those that are most relevant to the task at hand and that will provide the most predictive power.

3. Why is it important to understand your data before starting a project?

It is important to understand your data before starting a project because it will help you determine what features you will need to engineer in order to build a successful model. If you do not understand your data, you will not be able to identify which features are important, and you will not be able to build a model that accurately predicts your desired outcome.

4. What are some common ways to gather domain knowledge?

One common way to gather domain knowledge is to interview experts in the field. This can help you to understand the problem domain and identify relevant features. Another common way to gather domain knowledge is to perform a literature review. This can help you to identify relevant papers and research that can inform your feature engineering process.

5. How do you overcome challenges with missing data?

There are a few ways to overcome challenges with missing data. One is to simply impute the missing values, either with the mean or median of the column or using a more sophisticated technique like k-nearest neighbors. Another is to use a technique like decision trees, which can handle missing values without imputation. Finally, you can also try to avoid using features with a lot of missing values in your model altogether.

6. Can you explain how overfitting happens during model training?

Overfitting happens when a model is too closely fit to the training data, and as a result, does not generalize well to new data. This can happen if the model is too complex, or if the training data is not representative of the true underlying distribution. Overfitting can lead to poor performance on out-of-sample data.

7. What can be done if you find that most of your features have no predictive power on your target variable?

If you find that most of your features have no predictive power on your target variable, you can try to remove them and see if that improves your model’s performance. You can also try to create new features that might be more predictive. Finally, you can try to use a different machine learning algorithm that might be better suited to your data.

8. What’s the difference between feature extraction and feature selection? When should each one be used?

Feature extraction is the process of taking raw data and transforming it into features that can be used for machine learning. Feature selection is the process of selecting a subset of features to use for training a machine learning model. Feature selection should be used when you have a large number of features and want to select the most relevant ones, or when you want to reduce the dimensionality of your data. Feature extraction should be used when you want to transform your data into a form that is more suitable for machine learning.

9. If there are two correlated variables, which one should you keep?

If the two variables are highly correlated, then you should keep the one that is more predictive. If the correlation is not as strong, then you should keep both variables and let the model choose which one is more predictive.

10. What are some different techniques for dealing with categorical variables?

There are a few different ways to deal with categorical variables, depending on the type of data you are working with. For example, if you have a lot of categorical data that is ordinal (like “low,” “medium,” and “high”), then you can use techniques like one-hot encoding or label encoding. If you have categorical data that is not ordinal, then you might want to use a technique like dummy encoding.

11. What are some ways to deal with sparse data?

There are a few ways to deal with sparse data:

– One way is to simply ignore the data that is missing. This can be done by either discarding the entire row or column that contains missing data, or by imputing the missing values with the mean, median, or mode of the remaining values.
– Another way to deal with sparse data is to use a technique called feature selection, which essentially means choosing a subset of the features to use in the model. This can be done using a variety of methods, such as forward selection, backward selection, or a combination of the two.
– Finally, you could also use a technique called feature engineering, which involves creating new features from the existing data. For example, you could combine two or more features to create a new feature that is less likely to be sparse.

12. How can imbalanced datasets affect machine learning models?

Imbalanced datasets can cause machine learning models to be biased towards the majority class. This can lead to poorer performance on the minority class, and ultimately to poorer overall performance on the dataset as a whole. To avoid this, it is important to either balance the dataset before training the model, or to use a model that is designed to handle imbalanced datasets.

13. Do all features need to be scaled when using machine learning algorithms?

No, not all features need to be scaled. In general, features that are on a similar scale will work better with machine learning algorithms, but there are some algorithms that are scale-invariant. For example, tree-based algorithms are not affected by feature scaling.

14. What are some methods available for selecting features from a large dataset?

Some methods available for selecting features from a large dataset are:

-Remove features with low variance
-Remove features with high correlation
-Use a feature selection algorithm

15. Can you give me an example of where feature scaling would be required?

Feature scaling is often required when working with machine learning algorithms. This is because many machine learning algorithms require that the data is in a certain range in order for them to work properly. For example, if you are working with a neural network, the data needs to be scaled between 0 and 1 in order for the algorithm to converge.

16. What does it mean to bin numerical data? When should we use this technique?

Binning is a technique used to group together numerical data into “bins” or categories. This can be useful when we want to group data together for analysis, or when we want to reduce the amount of data points in a dataset. However, we need to be careful when binning data, as it can sometimes lead to information loss.

17. How do you handle mixed-type data types in Python?

One way to handle mixed-type data is to use a library like pandas, which provides a number of functions for working with data of mixed type. Another way to handle mixed-type data is to convert it to a single data type, such as a string, before processing it.

18. What is the best way to select features in supervised learning problems?

There is no one-size-fits-all answer to this question, as the best way to select features in supervised learning problems will vary depending on the specific problem and data set. However, some common methods for feature selection include using domain knowledge to select relevant features, using feature selection algorithms, and using cross-validation to compare different feature sets.

19. What are some good rules of thumb for applying transformations to numeric data?

A good rule of thumb is to always start with the simplest possible transformation and then move on to more complex ones if needed. For example, if you have a dataset with a lot of outliers, you might start by trying to remove them. If that doesn’t work, you could try transforming the data to make it more normally distributed. And so on.

20. Can you explain what dimensionality reduction is?

Dimensionality reduction is the process of reducing the number of features in a dataset while still retaining as much information as possible. This can be done through a variety of methods, such as feature selection or feature extraction.