Example response for question delving into Machine Learning 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: Classification is a supervised learning task that involves categorizing data into predefined classes or labels, while regression is also a supervised learning task that predicts continuous numerical values
•  Output: Classification produces discrete outputs, assigning data points to specific classes or categories, whereas regression produces continuous outputs, estimating numerical values
•  Target Variable: In classification, the target variable is categorical, representing different classes or labels. In regression, the target variable is continuous, representing a range of numerical values
•  Model Type: Classification models include decision trees, random forests, support vector machines, and logistic regression. Regression models include linear regression, polynomial regression, and decision trees
•  Evaluation Metrics: Classification models are evaluated using metrics like accuracy, precision, recall, and F1 score, which measure the model's performance in correctly classifying data points. Regression models are evaluated using metrics like mean squared error (MSE), root mean squared error (RMSE), and R-squared, which measure the model's accuracy in predicting numerical values
•  Application: Classification is commonly used for tasks like spam detection, sentiment analysis, and image recognition, where the goal is to assign data points to specific categories. Regression is used for tasks like sales forecasting, price prediction, and demand estimation, where the goal is to predict numerical values
•  Data Representation: Classification often involves categorical or binary features, and the data is represented in the form of feature vectors or matrices. Regression typically involves numerical features, and the data is represented as a set of input-output pairs
•  Decision Boundary: In classification, the decision boundary separates different classes or categories in the feature space. In regression, there is no clear decision boundary as the goal is to estimate a continuous value
•  Interpretability: Classification models are often more interpretable as they provide clear class labels or categories. Regression models may be less interpretable as they focus on estimating numerical values rather than assigning labels

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

•  Knowledge & understanding: Assessing the candidate's understanding of fundamental concepts in data science and their ability to differentiate between classification and regression
•  Technical expertise: Evaluating the candidate's proficiency in applying classification and regression techniques in real-world scenarios
•  Problem-solving skills: Assessing the candidate's ability to identify the appropriate modeling approach based on the nature of the problem, whether it requires classification or regression

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

•  Confusing or incorrect definitions: Providing inaccurate or unclear definitions of classification and regression
•  Lack of understanding of key differences: Failing to mention fundamental differences such as the type of output (discrete vs. continuous) or the goal of prediction (categories vs. values)
•  Inability to provide examples: Not being able to provide real-world examples or use cases for classification and regression
•  Failure to mention evaluation metrics: Neglecting to discuss evaluation metrics specific to classification (e.g., accuracy, precision, recall) or regression (e.g., mean squared error, R-squared)
•  Overgeneralization: Making broad statements without acknowledging exceptions or variations within classification and regression techniques

 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?
• What is the curse of dimensionality?
• How do you handle imbalanced datasets?
• Explain the concept of overfitting and how to prevent it.
• 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 concept of A/B testing.
• Explain the bias-variance tradeoff.
• What is feature engineering and why is it important?
• Explain the difference between bagging and boosting.
• What is the difference between precision and recall?
• How do you handle outliers in a dataset?
• What is the purpose of dimensionality reduction techniques?
• How do you deal with multicollinearity in regression?
• Explain the difference between L1 and L2 regularization.
• What is the purpose of cross-validation?
• What is the difference between bag-of-words and TF-IDF?
• What is regularization and why is it important?