Interview Questions and Answers

Feature engineering involves transforming raw data into a format that is more suitable for modeling. It includes tasks like scaling, normalization, and creating new features to improve the performance of machine learning models.

The activation function introduces non-linearity to a neural network, allowing it to learn complex patterns. Common activation functions include sigmoid, tanh, and ReLU.

Precision is the ratio of correctly predicted positive observations to the total predicted positives. It is a measure of the accuracy of positive predictions made by a classification model.

Regularization is used to prevent overfitting in machine learning models by adding a penalty term to the cost function. It discourages the model from fitting the training data too closely and encourages generalization to new, unseen data.

A confusion matrix is a table used to evaluate the performance of a classification model. It compares the predicted and actual class labels, showing true positives, true negatives, false positives, and false negatives.

Hyperparameters are configuration settings for machine learning models that are not learned from the data but are set before the training process. Examples include learning rate, regularization strength, and the number of hidden layers in a neural network.

One-hot encoding is a technique used to represent categorical variables as binary vectors. Each category is represented by a unique binary value, with only one bit set to 1 and the rest set to 0. It is commonly used in machine learning algorithms that cannot work directly with categorical data.

A confusion matrix is a table that summarizes the performance of a classification algorithm. It shows the number of true positives, true negatives, false positives, and false negatives, providing insights into the model's accuracy, precision, recall, and other metrics.