Data Science Interview Questions and Answers
Experienced / Expert level questions & answers
Ques 1. 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 2. 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.
Ques 3. Explain the term 'hyperparameter tuning' in the context of machine learning.
Hyperparameter tuning involves optimizing the hyperparameters of a machine learning model to achieve better performance. Techniques include grid search, random search, and more advanced methods like Bayesian optimization.
Example:
Adjusting the learning rate and the number of hidden layers in a neural network to maximize accuracy.
Ques 4. What is cross-entropy loss, and how is it used in classification models?
Cross-entropy loss measures the difference between the predicted probabilities and the actual class labels. It is commonly used as a loss function in classification models, encouraging the model to assign higher probabilities to the correct classes.
Example:
In a neural network for image classification, cross-entropy loss penalizes incorrect predictions with low probabilities.
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