All of the above techniques can be used to improve the performance of a classification model by enhancing data quality and model training.

All of the mentioned techniques can help mitigate overfitting in machine learning models.

Regularization techniques like Lasso or Ridge can help prevent overfitting by adding a penalty for larger coefficients.

Dropout is a regularization technique that randomly sets a fraction of input units to zero during training to prevent overfitting.

Using more training data can help prevent overfitting by providing a more comprehensive representation of the underlying data distribution.

Cross-validation is a technique that helps prevent overfitting by ensuring the model's performance is evaluated on different subsets of the data.

Dropout is a regularization technique that randomly sets a fraction of the neurons to zero during training to prevent overfitting.

K-Means Clustering is primarily a clustering technique, not a feature selection method.

Gradient descent is an optimization algorithm, not a tokenization technique.

K-Means Clustering is an unsupervised learning algorithm used for clustering, not for feature selection.

Principal Component Analysis (PCA) is a widely used technique for reducing the dimensionality of data.

Pruning the tree involves removing sections of the tree that provide little power in predicting target variables, thus preventing overfitting.

Dropout layers randomly deactivate neurons during training, which helps prevent overfitting.

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

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

Gradient Descent is the most commonly used optimization algorithm for minimizing the loss function in neural networks.

Stochastic Gradient Descent (SGD) is a popular optimization algorithm used to update weights in neural networks based on the gradient of the loss function.

Logistic Regression is a common algorithm used for binary classification tasks, predicting the probability of a binary outcome.

Imputation is a technique used to fill in missing values in a dataset.

To handle multicollinearity, one can increase the sample size, remove one of the correlated variables, or use more complex models like Ridge regression.

Applying regularization techniques can help prevent overfitting by penalizing overly complex models.

Cross-validation helps assess model performance on unseen data, reducing the risk of overfitting.

Dropout is a regularization technique that randomly sets a fraction of the neurons to zero during training to prevent overfitting.

Imputation is the technique used to fill in missing values in a dataset.

TensorFlow Serving is a popular tool for deploying machine learning models as APIs, allowing for easy integration into applications.

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

Git is widely used for version control in MLOps to track changes in code and model versions.

Convolutional Neural Networks (CNNs) are specifically designed for processing structured grid data like images, making them ideal for image recognition.

Convolutional Neural Networks (CNNs) are specifically designed for processing and analyzing visual data.

Convolutional Neural Networks (CNNs) are specifically designed for processing structured grid data like images, making them ideal for image recognition.