Interview Questions and Answers

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.

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.

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.

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.