Data Science Pertanyaan dan Jawaban Wawancara
Ques 6. What is the curse of dimensionality?
The curse of dimensionality refers to the challenges and increased computational requirements that arise when working with high-dimensional data. As the number of features increases, the data becomes more sparse, making it harder to generalize patterns.
Example:
In high-dimensional spaces, data points are more spread out, and distance metrics become less meaningful.
Ques 7. Explain the term 'feature engineering' in the context of machine learning.
Feature engineering involves selecting, transforming, or creating new features from the raw data to improve the performance of machine learning models. It aims to highlight relevant information and reduce noise.
Example:
Creating a new feature 'days_since_last_purchase' for a customer churn prediction model.
Ques 8. What is cross-validation, and why is it important?
Cross-validation is a technique used to assess a model's performance by splitting the data into multiple subsets, training the model on some, and evaluating it on the others. It helps estimate how well a model will generalize to new data.
Example:
K-fold cross-validation divides data into k subsets; each subset is used for both training and validation in different iterations.
Ques 9. Differentiate between bias and variance in the context of machine learning models.
Bias refers to the error introduced by approximating a real-world problem, and variance refers to the model's sensitivity to fluctuations in the training data. Balancing bias and variance is crucial for model performance.
Example:
A linear regression model might have high bias if it oversimplifies a complex problem, while a high-degree polynomial may have high variance.
Ques 10. What is regularization in machine learning, and why is it necessary?
Regularization is a technique used to prevent overfitting by adding a penalty term to the model's cost function. It discourages overly complex models by penalizing large coefficients.
Example:
L1 regularization (Lasso) penalizes the absolute values of coefficients, encouraging sparsity in feature selection.
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