Decision Trees are easier to interpret, making them preferable when interpretability is crucial.

Linear regression is preferred when predicting a continuous outcome variable based on one or more continuous predictor variables.

Random Forest is preferred for high accuracy and robustness, especially in larger datasets where overfitting is a concern.

Random Forests are preferred when higher accuracy and robustness are needed, especially in larger datasets.

A serverless architecture is preferred when you want to minimize operational overhead, as it automatically scales based on demand.

Linear regression is preferred when predicting a continuous outcome variable that has a linear relationship with the independent variables.

LSTMs are preferred when dealing with sequences that have long-term dependencies due to their ability to remember information over time.

Support Vector Machines are particularly effective in high-dimensional spaces, making them suitable for datasets with many features.

SVM is preferred in high-dimensional and sparse datasets, as it can effectively find the optimal hyperplane in such spaces.

SVMs are particularly effective in high-dimensional spaces, making them suitable for datasets with many features.

SVM is particularly effective in high-dimensional spaces, making it suitable for datasets with many features.

SVM is particularly effective in high-dimensional spaces, making it suitable for datasets with many features.

The Matthews correlation coefficient is a robust metric that can be used for both binary and multi-class classification, especially in imbalanced datasets.

In scenarios where missing a positive case (false negative) is more critical, such as in medical diagnoses, recall should be prioritized over precision.

CNNs are primarily used for classifying images due to their ability to capture spatial hierarchies in visual data.

CNNs are specifically designed for processing structured grid data like images, making them ideal for image classification tasks.

Shadow deployment allows testing a new model in production without affecting the user experience, as it runs alongside the existing model.

Linear regression is suitable for forecasting sales revenue based on advertising spend, as it involves predicting a continuous variable.

Reinforcement learning is used when a model learns from interactions with an environment, optimizing actions based on rewards.

Unsupervised learning is used to discover patterns in unlabeled text, such as clustering or generating embeddings.

One of the key assumptions of linear regression is that the residuals (errors) should be normally distributed.

'Bagging' refers to the technique of combining multiple models to improve overall performance and reduce variance.

An epoch is one complete pass through the entire training dataset during the training process of a neural network.

Model drift occurs when the statistical properties of the target variable change over time, leading to decreased model performance.

Model drift refers to the phenomenon where a model's performance decreases over time due to changes in the underlying data distribution.

Overfitting occurs when a model learns the training data too well, including noise, resulting in poor generalization to new data.

Training a neural network involves adjusting its weights based on labeled data to minimize the loss function.

A confusion matrix provides a summary of prediction errors by showing the counts of true positives, true negatives, false positives, and false negatives.

A confusion matrix provides a summary of prediction results, showing true positives, false positives, true negatives, and false negatives.

A high AUC value indicates that the model has good discrimination ability between classes.