Mean Absolute Error (MAE) is a common evaluation metric for regression models, measuring the average magnitude of errors in predictions.

Accuracy is a common evaluation metric used to assess the performance of SVM classifiers by measuring the proportion of correctly classified instances.

Mean Squared Error (MSE) is commonly used as a loss function for regression tasks.

Cross-Entropy Loss is widely used for classification tasks as it measures the difference between predicted and actual class probabilities.

Common methods for deploying machine learning models include both batch processing and real-time inference, depending on the application requirements.

Label Encoding is a common method for converting categorical variables into numerical format.

A confusion matrix is used to evaluate the performance of a classification model by showing true vs. predicted classifications.

Principal Component Analysis (PCA) is a technique used to reduce dimensionality by extracting important features.

Oversampling the minority class is a common method to address class imbalance, helping the model learn better from the underrepresented class.

Replacing missing values with the mean or median is a common method, though other methods can also be used depending on the context.

Imputing missing values with the mean or median is a common method to handle missing data, allowing for the retention of more data points.

Imputing missing values with the mean or median is a common method to handle missing data.

Grid Search is a common method for systematically searching for the best model parameters.

Pruning the tree after it has been fully grown helps to remove branches that have little importance, thus preventing overfitting.

Word2Vec is a popular method for generating word embeddings that captures semantic relationships between words.

Neural networks are often used to represent the policy in reinforcement learning due to their ability to approximate complex functions.

Gradient Descent is a widely used optimization algorithm for minimizing the loss function in neural networks.

Principal Component Analysis (PCA) is commonly used for feature selection by reducing dimensionality while retaining variance.

Mean imputation is a common technique where missing values are replaced with the mean of the available data.

Principal Component Analysis (PCA) is used to reduce dimensionality and select important features.

Using dropout layers is a common technique to prevent overfitting by randomly setting a fraction of input units to 0 during training.

Principal Component Analysis (PCA) is a dimensionality reduction technique that can also be used for feature selection by identifying the most important features.

TensorFlow Serving is a popular tool specifically designed for deploying machine learning models in production environments.

Decision Trees are commonly used for customer segmentation due to their ability to handle categorical data effectively.

Random Forests can be applied to various tasks, including image recognition, time series forecasting, and spam detection.

Churn prediction is a common application of supervised learning in marketing, helping businesses identify customers likely to leave.

Decision Trees are prone to overfitting, especially when they are deep and complex.

All of the listed options are disadvantages of K-means clustering, making it sensitive to outliers, requiring prior knowledge of the number of clusters, and potentially converging to local minima.

A key disadvantage of K-means is that it requires the user to specify the number of clusters beforehand, which may not always be known.

Decision trees can easily overfit the training data, especially if they are not pruned or if the tree is too deep.