Artificial Intelligence (AI) preguntas y respuestas de entrevista
Pregunta 41. What is the role of dropout in preventing overfitting in neural networks?
Dropout randomly deactivates a fraction of neurons during training, making the model more robust by preventing reliance on specific neurons. This helps prevent overfitting by promoting generalization.
Example:
Applying dropout to hidden layers in a neural network during training.
Pregunta 42. How can you handle imbalanced datasets in machine learning?
Handling imbalanced datasets involves techniques such as resampling (oversampling minority class or undersampling majority class), using different evaluation metrics, or applying specialized algorithms designed for imbalanced data.
Example:
In fraud detection, where only a small percentage of transactions are fraudulent, employing techniques to address the class imbalance.
Pregunta 43. What is the difference between batch gradient descent and stochastic gradient descent (SGD)?
Batch gradient descent calculates the gradient of the entire dataset before updating model parameters, while SGD updates the parameters after each training example. Mini-batch gradient descent is a compromise between the two, using a subset of the data.
Example:
Updating weights in a neural network after processing a single training example (SGD) versus the entire dataset (batch gradient descent).
Pregunta 44. What is a confusion matrix, and how is it used to evaluate classification models?
A confusion matrix is a table that compares the actual and predicted classifications of a model, displaying true positives, true negatives, false positives, and false negatives. It is useful for assessing model performance, especially in classification tasks.
Example:
Evaluating a binary classification model using a confusion matrix that shows correct and incorrect predictions.
Pregunta 45. How can you handle missing data in a dataset?
Handling missing data can involve techniques such as imputation (replacing missing values with estimated values), removing instances with missing values, or using algorithms that can handle missing data directly.
Example:
Replacing missing age values in a dataset with the mean age of the available data points.
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