Example response for question delving into Feature Engineering with the key points that need to be covered in an effective response. Customize this to your own experience with concrete examples and evidence

•  Definition of Feature Engineering: Feature engineering is the process of transforming raw data into a format that is suitable for machine learning algorithms. It involves creating new features or modifying existing ones to improve the performance of a predictive model
•  Importance of Feature Engineering: Feature engineering is important for several reasons:
•  Improved Model Performance: By creating relevant and informative features, feature engineering can significantly improve the performance of machine learning models. It helps the model to better capture patterns and relationships in the data, leading to more accurate predictions
•  Handling Missing Data: Feature engineering techniques can be used to handle missing data. For example, missing values can be imputed using statistical measures such as mean, median, or mode. This ensures that the model can still make use of the available data
•  Dimensionality Reduction: Feature engineering can help in reducing the dimensionality of the data. By selecting or creating the most relevant features, unnecessary or redundant features can be eliminated. This not only reduces computational complexity but also improves model interpretability
•  Handling Non-linearity: Feature engineering can transform non-linear relationships into linear ones, making it easier for models that assume linearity to learn from the data. Techniques like polynomial features, logarithmic transformations, or interaction terms can be used to capture non-linear patterns
•  Feature Scaling: Feature engineering can involve scaling or normalizing features to a common range. This is important for models that are sensitive to the scale of the input features, such as distance-based algorithms. Scaling ensures that all features contribute equally to the model's learning process
•  Domain Knowledge Incorporation: Feature engineering allows domain knowledge to be incorporated into the model. By creating features that are relevant to the problem domain, the model can leverage prior knowledge and improve its predictive capabilities
•  Reducing Overfitting: Feature engineering can help in reducing overfitting by regularization techniques. For example, feature selection methods like L1 regularization can be used to select the most important features, preventing the model from memorizing noise or irrelevant patterns
•  Iterative Process: Feature engineering is an iterative process that involves experimenting with different transformations, combinations, and selections of features. It requires a deep understanding of the data and the problem domain, as well as continuous evaluation and refinement of the engineered features

  What the Interviewer is trying to find out about you and your experiences through this question

•  Knowledge & understanding of data science techniques: To assess the candidate's understanding of feature engineering as a fundamental technique in data science
•  Problem-solving skills: To evaluate the candidate's ability to identify and create relevant features that can improve model performance
•  Critical thinking: To gauge the candidate's ability to think analytically and creatively in order to engineer meaningful features from raw data
•  Domain expertise: To determine if the candidate can leverage their knowledge of the specific technology function to engineer features that capture relevant information

  How to avoid some common minefields when answering this question in order to not raise any red flags

•  Lack of understanding: Not being able to provide a clear definition of feature engineering or its purpose
•  Vague or generic response: Providing a general explanation without specific examples or details
•  Limited knowledge: Inability to discuss the various techniques and methods used in feature engineering
•  Ignoring the importance: Downplaying the significance of feature engineering in data science tasks

 Related

  Other questions asked for the Data Scientist in Technology function. View details for the Data Scientist here

• What is the difference between supervised and unsupervised learning?
• How do you handle missing data in a dataset?
• How do you handle imbalanced datasets?
• How do you select the optimal number of clusters in K-means clustering?
• What is the purpose of a validation set in machine learning?
• Explain the difference between bagging and boosting.
• What is the purpose of dimensionality reduction techniques?
• How do you deal with multicollinearity in regression?
• How do you handle outliers in a dataset?
• What is the difference between precision and recall?
• Explain the difference between L1 and L2 regularization.
• What is the difference between classification and regression?
• What is the purpose of cross-validation?
• Explain the concept of overfitting and how to prevent it.
• What is the difference between bag-of-words and TF-IDF?
• What is regularization and why is it important?
• What is the curse of dimensionality?
• Explain the concept of A/B testing.
• Explain the bias-variance tradeoff.